The final, definitive version of this paper has been published in Science Communication, Online first, March 2017 published by SAGE Publishing, All rights reserved. http://journals.sagepub.com/doi/abs/10.1177/1075547017696165

Citizen science as a means for increasing public engagement in science: presumption or possibility?

Victoria Y. Martin1,2

Citizen science is often assumed to increase public science engagement, however little is

known about who is likely to volunteer and the implications for greater societal impact. This

study segments 1145 potential volunteers into six groups according to their current

engagement in science (EiS). Results show groups with high levels of EiS are significantly

more interested in volunteering and more likely to participate in various research roles than

those with lower EiS scores. While citizen science benefits some in science and society, its

use as a strategy to bring about positive shifts in public science engagement needs to be

reconsidered.

Keywords: science engagement, public participation in scientific research, public interest,

social survey, audience research

Introduction

The disciplinary discourse, policy and practice of science communication has seen the push

for public understanding of science shift to a push for public engagement in science (Davies,

2013; Irwin, 2014; Selin et al., 2016; Stilgoe, Lock, & Wilsdon, 2014). Although public

engagement in science (PES) has many different meanings in many different contexts 1Southern Cross University, Lismore, New South Wales, Australia2Cornell University, Ithaca, NY, USA

Corresponding Author:Victoria Y. Martin, Cornell Lab of Ornithology, 159 Sapsucker Woods Rd., Ithaca, NY 14850, USA.Email: vicki.martin@scu.edu.au

(discussed below), a scientifically engaged society is seen as essential for delivering

democratic science governance and decision-making, and empowering individuals and

communities to be aware of, and able to use science in their everyday lives (Árnason, 2013;

Bickerstaff, Lorenzoni, Jones, & Pidgeon, 2010; Irwin, 1995, 2014). How deeply an

individual is engaged in science has implications for their ability to make scientifically-

informed decisions (Irwin, 1995; Phillips, Carvalho, & Doyle, 2012) about issues such as

nanotechnology (Anderson, Delborne, & Kleinman, 2013; Powell & Lee Kleinman, 2008),

genetically modified food (Irwin, 2006; Nielsen, Lassen, & Sandøe, 2011), vaccinations

(Kata, 2012), and climate change (Jaspal, Nerlich, & van Vuuren, 2015). For these reasons,

governments and scientific organizations across the globe are supporting activities to improve

the public’s relationship with science (see, for example: the public engagement goals of the

EU's "Horizon 2020", www.ec.europa.eu/programmes/horizon2020; the USA’s Center for

Public Engagement with Science & Technology, www.aaas.org/pes/what-public-engagement;

and Australia’s national science engagement strategy, www.inspiringaustralia.net.au).

One method for the public to engage directly is citizen science, which describes research

projects that involve volunteers in some capacity during the scientific process. This paper

investigates the link between public willingness to participate in citizen science and the

impact this is likely to have on the public’s broader engagement in science. Placed in the

context of marine science in Australia, this study analyses data from a national survey of

marine users (i.e. fishers, divers, beach users etc.) about public participation in marine

research. The research is distinct in its approach to this issue, through its focus on potential

volunteers for marine citizen science rather than only volunteers who have already been

recruited in citizen science projects. The specific aim of the paper is to understand the

possibility for marine citizen science to attract volunteers who differ in their pre-existing

levels of engagement in science, since this has implications for the ability of citizen science

to affect broader societal shifts in the science/society relationship.

Public engagement in science versus public participation in science

Within the science communication literature, the terms public engagement and public

participation are sometimes used interchangeably. In the context of this paper however,

engagement and participation of the public in science are seen as having different meanings

due to differences in the directions of scientific knowledge flow (Figure 1). In essence, these

two concepts are similar to two of the three typologies of public engagement (i.e. public

communication and public participation) proposed by Rowe and Frewer (2005). Public

participation, as it is considered in this paper, extends the Rowe and Frewer (2005) model by

including public participation in scientific research (PPSR; Shirk et al., 2012). In the PPSR

model, the public are seen to be participating in the creation of scientific knowledge.

In contrast, public engagement in science (PES) sees the public as consumers of science

communication. Their level of engagement in science can be demonstrated by whether or not

they are interested in and pay attention to science communication (no matter the

communication method or reason for their interest; British Department for Business

Innovation & Skills [BIS], 2011; Department of Business and Innovation [DBI], 2012; Pew

Research Center, 2015; Reyes, 2013; Searle, 2014), how they interpret and understand the

information (Kahan et al., 2012; Lewandowsky & Oberauer, 2016; Stevenson, Peterson,

Bondell, Moore, & Carrier, 2014), to what extent they seek it out to use it for decision-

making (Scheufele, 2013), and so on.

Figure 1. Public engagement vs. public participation

Public participation in citizen science

PPSR encompasses the practice of citizen science, which can be described as “research

collaboration that involves volunteers in producing authentic scientific research” (Wiggins &

Crowston, 2015). Aside from conducting research, some citizen science projects also aim to

educate the public in science (Brossard, Lewenstein, & Bonney, 2005; Land-Zandstra,

Devilee, Snik, Buurmeijer, & van den Broek, 2016; Mayer, 2010) and expect these

experiential research activities to result in positive societal changes in the relationship

between science and society (Bonney, Phillips, Ballard, & Enck, 2015; Dickinson et al.,

2012; Haywood & Besley, 2014; Jordan, Gray, Howe, Brooks, & Ehrenfeld, 2011; Pecl,

Gillies, Sbrocchi, & Roetman, 2015).

There is often an implicit assumption that “non-scientist” volunteers come to citizen science

projects with relatively low levels of scientific knowledge or understanding, or (sometimes)

little support or interest in science. Yet studies of citizen scientists’ motivations indicate

many volunteers participate primarily to make contributions to science, which implies they

are at least somewhat “pro-science” (Curtis, 2015; Haywood, 2015; Johnson et al., 2014;

Raddick et al., 2013). In addition, volunteers in some citizen science projects have higher

levels of education than the general population (Brossard et al., 2005; Raddick et al., 2013).

Unfortunately the educational background of citizen science participants is rarely found in

published citizen science studies, so it is difficult to know whether this trend extends across

the field. Nevertheless, the indications in the literature bring into question the ability for

citizen science to affect broad societal change if it primarily recruits volunteers who are at the

higher end of the science engagement spectrum.

Justification for the study

This research examines potential volunteers from all levels of the science engagement

spectrum (although the higher groups are more readily represented in this study due to the

bias in the survey, discussed below). The difficulty in using broad definitions to describe

terms such “science” and the “public” (Burns & Medvecky, 2016) is acknowledged,

particularly as “science” can mean many different things in many different contexts, and the

term “public” does nothing to help a communicator understand the needs and interests of a

particular target audience. Yet, science communicators and public engagement strategies

remain tasked with not only defining target groups, but also with providing evidence to show

they have been successful. This necessitates a certain amount of quantitative research and

categorization to support evidence-based approaches to effective public engagement.

In addition, enthusiasm and support for citizen science is growing, often driven by

assumptions that public participation in it will be a “good thing” for the relationship between

science and society. For example, in November 2016 the Australian government announced it

will provide $4 million dollars in grants to support the growth of citizen science in Australia

(www.business.gov.au/assistance/inspiring-australia-science-engagement/citizen-science-

grants). These grants are available for projects that aim to “provide opportunities for the

public to engage in science” and form part of the national innovation initiative to “inspire

STEM literacy and engagement in all Australians”

(www.science.gov.au/community/Pages/Citizen-Science-Grants-announced). Australia’s

Office of the Chief Scientist also views citizen science in a favorable light, stating that it will

enhance Australians’ awareness of science (Pecl et al., 2015). Citizen science is also seen by

government organizations as having great potential to engage wide audiences in other parts of

the world such as Europe (Museum für Naturkunde, 2016) and the USA (Holdren, 2015).

While there does appear to be room for growing the number of citizen science volunteers, at

least in a marine context (Martin, Christidis, & Pecl, 2016), it is somewhat surprising that

considerable resources and effort are being provided for the further development of citizen

science without empirical evidence showing that it is likely to impact the types of people

these strategies aspire to reach. This study is a first step in the provision of such evidence,

which is necessary for assessment of the broader societal impacts arising from citizen

science.

Methodology

Given the focus of the Australian science engagement strategy, Inspiring Australia

(Department of Innovation, Industry, Science and Research [DIISR], 2010), this study took a

national approach to the research, using an online survey to gain responses across the

country. The development, testing and promotion of the survey has been reported elsewhere

(Martin, Christidis, & Pecl, 2016) however, for the purposes of this paper a summary of the

methods are provided here. The study focused on Australian marine users (that is, people who

use the beaches and oceans) since the Inspiring Australia strategy suggests marine science

offers opportunities to increase Australians’ engagement in science more broadly. Marine

science is emphasized in the strategy due to the strong connections and proximity most

Australians have with the marine environment (Department of Industry, Innovation, Science,

Research and Tertiary Education [DIISRTE], 2012).

National survey development

Several sources were used to inform the development of the survey questions employed in

this study. Background information was gathered on the marine users’ demographics, science

education, and interests in the marine environment to ensure a broad range of participants

were represented. Demographic questions were taken from the 2011 census of the Australian

population (sourced from www.abs.gov.au). Questions about people’s science education and

employment in the science industry were taken from the Victorian survey (Department of

Business and Innovation [DBI], 2012).

Questions used to measure levels of individual’s engagement in science (EiS) were modified

versions of the three survey questions used in the DBI (2012) and Cormick (2014) analyses.

These questions related to: (i) the individual’s interest in science, (ii) active searching for

science information, and (iii) ability to find and understand the information. In addition to

these questions, another was added to ask respondents about their trust in science due to the

considerable attention this issue receives in both academic (Achterberg, de Koster, & van der

Waal, 2015; Engdahl & Lidskog, 2014; Gauchat, 2011; Resnik, 2011; Wynne, 2006) and

mainstream publications (Lynas, 2015; Rutherford, 2015). The final questions (and

corresponding labels) used to develop the EiS score were:

(i) How interested are you in science, generally? (interest)

(ii) In general, how often do you actively seek out scientific information? (seek)

(iii) In general, how much do you trust scientific research? (trust)

(iv) In general, how easy do you find it to understand scientific information? (understand)

All responses were recorded on a 7-point scale (Table 1).

To determine the respondents’ willingness to participate in marine citizen science, another

group of questions (referred to as the PPSR questions in the analysis) were developed using

the term marine research instead of marine citizen science, since the latter is an unfamiliar

term for many Australians. The PPSR questions were grouped into two themes: (i) general

willingness to participate, and (ii) interest in specific research roles. Theme 1 questions were

designed to gain a measure of general interest in assisting marine research. Some of these

questions used 7-point scales to ask respondents about their level of interest in, and expected

enjoyment from participating in marine research. In addition, respondents were asked how

many hours per annum they are willing to volunteer for marine research. Theme 2 questions

presented respondents with a list of roles volunteers might perform in marine scientific

research, and asked them to record (on a 7-point scale) how likely they would volunteer for

each role. The list was based on the framework for PPSR developed by Shirk et al. (2012),

which describes the different ways in which volunteers participate in any, or all, aspects of

scientific research.

The questions used in the national survey were first tested during 110 face-to-face interviews

with marine users in four regions of Australia (Martin, Christidis, Lloyd, & Pecl, 2016). Once

the questions were refined and entered into the online survey platform (Qualtrics), they were

further tested by 12 people on a variety of devices in different locations across the country.

Minor wording changes were made for clarity. The survey was open for 8 weeks during

February – April 2015. It was promoted around the country using multiple snowball sampling

strategies (Bryman, 2012) in mainstream media (newspapers, radio, magazines), online and

social media (forums, Facebook, Twitter), and direct email campaigns using networks of

groups interested in the marine environment (clubs, special interest groups, etc.) as well as

groups with interests in marine research and citizen science (e.g. the Australian Citizen

Science Association, universities, etc.)

During the earlier face-to-face interview phase of the research, interviewees (which included

recreational fishers, divers, boaters, sailors, surfers, kayakers, life savers and beach walkers)

were asked whether they would be likely to complete a 30 minute online survey on the same

research topic. Many interviewees mentioned incentives would be necessary for such a

lengthy survey, and this was most noticeably the case for the interviewees with lower levels

of interest in science. Most of the interviewees suggested specific incentives related to their

preferred activity, such as a fishing rod. Lottery incentives, such as prize drawings, are not

uncommon in marketing-type surveys in Australia, and given the interviewees were very

particular about the types of prizes that would appeal to them, sponsors were sought to donate

relevant marine-related prizes.

In total, 20 prizes (together worth $2000) were offered, all of which were selected to appeal

to different target groups for the research. The prizes included a quality fishing rod and reel,

SCUBA diving vouchers, snorkeling equipment, an underwater scooter, marine life books

and DVDs, and marine communication equipment (an entertainment system for boats,

personal locator beacon, and mobile phone protectors). While the use of incentives is

controversial topic in social research, they have been found to increase participation in online

surveys (David & Ware, 2014, Edwards et al., 2009). Meta analyses have uncovered

variability in the effect of different types of incentives (e.g. cash incentives appear to be the

most effective, although Khadjesari, Z., et al. (2011) suggest the amount of cash paid to

respondents may influence the response rate). The approach taken in this study was driven by

the earlier discussions with the target audience, and was deliberate in its intention to appeal to

the harder-to-reach groups who were less likely to respond to such a survey.

The survey was commenced by 1375 people, of which 1145 were fully completed (83.3%

completion rate). Respondents represented a broad range of ages and interests in the marine

environment, and came from all states and mainland territories of Australia. Although it was

not intended to be representative of the Australian population, the most notable difference

between respondents and the 2011 population census data (sourced from www.abs.gov.au)

was the much higher proportion of postgraduate education for the survey respondents

(22.4%) compared to the census (5.2%). Half of the respondents (50.1%) had studied science

at the tertiary level, yet only around half of these people (equating to 26.7% of respondents)

were working in the science industry.

Analysis

After data screening and cleaning, the analysis proceeded in two main stages: (i) the

computation of the EiS scores and EiS groups, and (ii) the EiS groups were checked for

significant correlations with the PPSR measures and significant between-groups differences

in the mean PPSR scores. Prior to the formation of the EiS groups, the four EiS measures

(interest, seek, trust and understand) were checked for scale reliability using Cronbach’s

alpha (Field, 2013), which indicated the scale was acceptable (α = .823, which increases

to .855 if trust is removed). There were no issues of multicollinearity between the variables

(all correlations < .90, Field, 2013), there was no missing data, and all scales used were of

equal size (7-points). All scale items were found to be non-normal using both the Shapiro-

Wilk and Kolmogorov-Smirnov tests for normality, meaning non-parametric analyses were

necessary (Field, 2013). An exploratory factor analysis was conducted on the scale items

using principle axis factor extraction (Osborne & Costello, 2009). The analysis returned one

factor that included all of the variables, however it showed trust had a much lower factor

loading (.479) compared to interest (.862), seek (.856) and understand (.740). The

combination of the four items explained 56.33% of the variance, however when trust was

excluded this increased to 67.95% of the variance. For this reason, trust was removed from

the analysis. The Kaiser-Meyer-Olkin measure verified the sampling adequacy (KMO = .705,

which is well above the acceptable limit of .5; Field, 2013).

Before determining EiS group membership, the data was split into two groups: working

scientists (which includes all respondents working in the science industry, of which 91.5%

studied science in higher education and the remainder are likely to be administrative or

technical staff in scientific organizations) and non-scientists. This was done for two reasons:

(i) working scientists or staff in scientific organizations are more likely to become involved in

research through their own work interests, either voluntarily or through their employment

(however it was noted this group responded across the full scale of the EiS questions, with

the majority at the more favorable end of the scale), and (ii) citizen science practice often

claims to engage “non-scientists”, so it is important to conduct the analysis on the potential to

engage this group specifically.

The EiS scores for non-scientists were calculated by summing the responses to the three EiS

questions, each of which used a 7-point scale (resulting in a possible score range of 3-21)

which were then used to group respondents. The group EiS score cutoff points were based on

the range of scores corresponding to relevant levels on the measurement scale, taking into

account the negative skew in the responses (all EiS questions and scales are presented in

Table ). The final EiS groups were assigned descriptors defined as: low (scores ranging from

3-11, i.e. all fell below the midpoint of the scale; N = 67, M = 9.22, SD = 2.01), ambivalent

(scores ranging 12-14, i.e. around the midpoint of the scale; N = 164, M = 13.17, SD = .79),

moderate (scores between 15-17; N = 275, M = 16.03, SD = .78), high (scores between 18-20;

N = 267, M = 19.04, SD = .81), very high (score of 21, i.e all respondents who selected 7 on

every scale; N = 56, M = 21.00, SD = .00), and working scientists (N = 316, M = 19.22, SD =

2.06).

The six EiS groups were then assessed for differences in demographics and their responses to

the PPSR questions. All statistical tests were carried out according to Field (2013).

Correlations using Spearman’s rho and bootstrapping were calculated between EiS scores and

PPSR variables. Differences in means were tested using the Kruskal-Wallis test and post-hoc

analysis was performed using the pairwise comparison procedure. Effect size for each

pairwise comparison was calculated using eta squared. SPSS 22 was used to run all analyses.

Results

The five non-scientist EiS groups describe broad audience types by their EiS measurements,

ranging from low to very high engagement in science (Table 2). In general terms, the higher

the EiS group membership, the higher the level of education generally, and science education

specifically. All groups have more males than females. The low and ambivalent groups have

a greater proportion of respondents older than 45 years compared to the higher-level EiS

groups.

EiS group differences in PPSR questions

General interest in citizen science participation

The majority of respondents to this survey are willing to assisting scientific research in some

way (this could be anything from medical research to filling in surveys), however while most

of the higher EiS groups are “definitely interested” there is a relatively high percentage of

respondents in the low (71.6%) and ambivalent (50.0%) EiS groups who answered “maybe”

to this question (). The number of hours respondents are willing to volunteer for marine

research represents considerable volunteer effort, however the higher EiS groups are willing

to donate more hours per annum overall than lower EiS groups.

The responses to the questions about level of interest, enjoyment and confidence in their own

ability to assist marine research show a low positive correlation with EiS group membership.

Responses to these questions were significantly different between the groups (see H and p

values in Table 3), and pairwise comparisons of group means for these questions showed

statistically significant differences in the responses of the high, very high and working

scientists compared to the lower EiS groups (see notes for Table 3). Working scientists and

the very high EiS group also stood out as having the strongest confidence in their ability to

assist marine research. Large effect sizes occurred between low – high, and low – very high

EiS groups for these three questions, as well as between ambivalent – very high for the

enjoyment and confidence questions. The majority of effect sizes for the remaining groups

were medium to low1.

1A full list of the effect sizes of difference in EiS group mean scores for PPSR variables may be obtained from the author.

Interest in specific roles in PPSR

The likelihood of respondents participating in the different roles in PPSR (measured on a 7-

point scale where 1 = very unlikely and 7 = very likely) shows a low positive correlation with

EiS group membership (Table ). Group means for each of the PPSR questions were

significantly different (see H and p values in Table 4), and increase with EiS group

membership up to the very high group, after which it drops slightly (but not significantly) for

the working scientists group. Mean scores below 4 on the scale indicate groups are unlikely to

participate in particular roles. This is the case for all roles except data collection in the low

EiS group. The ambivalent and moderate group means hover around the midpoint (between

3-5) for most roles, except data collection which increases above 5. Data collection is also the

most preferred role for all remaining groups. Data analysis and communication of the

findings are either the second or third most preferred roles for the high, very high, and

working scientist groups (all receiving scores above 5.26). The very high group is the only

group to score above 5 for all PPSR roles.

Statistically significant differences occur between groups for all PPSR roles. There is a clear

difference between the lower (low – moderate) EiS groups and the higher (high – working

scientists) groups for all roles except two, and group differences also occur between the lower

and higher groups. The two exceptions to this result are data collection and data analysis

roles, both of which differ significantly between the lower two groups (low – ambivalent) and

the remaining (moderate – working scientists) groups (in other words, the top four groups

rated their likelihood of participation significantly higher than the bottom two groups).

In summary, the six EiS groups vary significantly in their engagement in science, and

potential engagement in PPSR activities. The tests for group differences in their interest and

likelihood of participation in marine research show PPSR opportunities appeal most to people

with pre-existing high levels of EiS, and it is unlikely (although not impossible) to attract

volunteers whose EiS score is low.

Discussion

This study segmented marine users into six audience types based on their current level of

engagement in science. It detected discernible differences in their general interest in

volunteering for marine research as well as their likelihood of participation in a range of

marine research roles which are proposed within the PPSR framework. This information is

useful for an evidence-based approach to strategies employing citizen science for the purpose

of increasing public engagement in science. It is especially important to bear in mind when

assumptions are made about the ability and usefulness of citizen science in bringing about

societal impacts in the low or ambivalent EiS groups.

Limitations

While this research attempted to gain broad perspectives on Australian marine users’ level of

interest in citizen science, there are three important limitations to this study. First, its focus on

the marine environment means the findings are very context-specific. This raises the question

about the applicability of the results to terrestrial, freshwater, and virtual environments,

where a great deal of citizen science activity also occurs. However, the indications so far

suggest that mainstream citizen science across many different fields lacks socio-economic

and educational diversity amongst volunteers (Hobbs & White, 2012; Pandya, 2012; Raddick

et al., 2013; Soleri, Long, Ramirez-Andreotta, Eitemiller, & Pandya, 2016). The results

presented here support this important finding, which is particularly concerning for the

expectations of some for societal impacts arising from citizen science.

The second important limitation to this research arises from the use of a lottery incentive to

recruit respondents. While this approach was informed by consultation with the target

audience prior to the release of the survey, it may have resulted in attracting only respondents

who are motivated by material incentives rather than altruistic reasons. The study aimed to

gain responses nation-wide from marine users with diverse interests in science, especially

people in the lower EiS groups who are a notoriously difficult group to study (Cormick,

2013; Evans & Plows, 2007; Powell, Colin, Lee Kleinman, Delborne, & Anderson, 2010;

Stevenson, Sikich, & Gold, 2012). Although these groups are likely to be underrepresented in

this research, there is enough variation in the results to at least shed some light on

engagement with this group, and the use of incentives helped to achieve these responses.

The third limitation relates to the natural bias arising from snowball sampling, which results

in groups with similar interests (in this case, an interest in participating in marine research)

responding to the survey. Despite effort to craft survey recruitment messages to convey that

the study aimed to survey people who are, and people who are not willing to assist marine

research, it is not surprising to receive a greater proportion of those who are. This may

account for the overrepresentation of respondents with postgraduate degrees and higher

education in science. Separating the working scientists in the analysis helped to remove the

impact of this limitation.

Who is most likely to volunteer in citizen science?

The EiS groups differ in many of their background factors, particularly their level of higher

education and education in science. The very high EiS group is the most interested in

assisting scientific research, and the most likely to participate in PPSR activities. This group

is also the most highly educated. This finding parallels other social research in citizen science

and environmental volunteers, which has revealed volunteers tend to have educational

qualifications which are higher than average (Brossard et al., 2005; Dresner, Handelman,

Braun, & Rollwagen-Bollens, 2015; Evans et al., 2005; Raddick et al., 2013). This study also

shows people who are most likely to volunteer for marine research may also have higher

education and training in science, yet are not necessarily working as scientists. This may

partially explain why those who participate in citizen science do not necessarily change their

(already positive) attitude towards science following the experience (Brossard et al., 2005).

The results also highlight the valuable knowledge and skills enthusiastic volunteers may

bring to citizen science; a detail which should not be disregarded or undervalued. The keenest

volunteers are not necessarily, as some suggest, simply “lay people”, “non-scientists”, or

even “non-professional scientists” (Evans & Plows, 2007; Foster-Smith & Evans, 2003;

Haklay, 2013; Lewenstein, 2004). The assumption a “citizen scientist” has little

understanding of scientific processes may be incorrect in some circumstances, and may

contribute to the perception of some professional scientists that citizen science data is not

reliable or robust (Cohn, 2008; Fore, Paulsen, & O' Laughlin, 2001; Gollan, 2013; Newman,

Buesching, & Macdonald, 2003). Definitions of “citizen scientists” need to acknowledge

some volunteers may be professional scientists (or at least tertiary trained in science), who

are willing to perform unpaid roles in research projects.

The ability of marine citizen science to recruit many more volunteers who are already pro-

science is a positive outcome for the future of citizen science (since highly skilled volunteers

can be very useful). This scenario will help to address the urgent need to broaden the spatial

and temporal scale of marine research and improve our understanding of the rapid changes

occurring in the world’s oceans (Healey, 2016; Koslow & Couture, 2015; Marzloff et al.,

2016; McCauley et al., 2015). However, the results of this study bring into question the

realistic ability for marine citizen science to engage Australians who are less than enthusiastic

about science.

Engagement of other EiS groups

While the very high EiS group is the “low hanging fruit” for marine citizen science

recruitment, there is strong potential to engage the moderate and high groups, bearing in

mind they feel less able to understand science, and are less confident in their ability to assist

marine research than the very high and working scientists groups. In another analysis of data

from this survey, which used a case study of a project that asked for photographic records of

marine species distributions, the only significant barrier was people’s perceived lack of

knowledge about marine species (Martin, Smith, et al., 2016). Since all groups were most

interested in helping to collect data, projects which combine data collection and opportunities

to increase volunteers’ knowledge of marine species may be important gateways for

recruitment of the moderate and high EiS groups.

Helping potential volunteers overcome their perceived knowledge deficit will require

education and training. For some projects, volunteers need very little knowledge, in which

case it will be important this is communicated in recruitment messages. For other projects,

volunteer education might take several different forms, and should be determined through

research with the target groups. Examples include species identification guides (or similar

guides appropriate to the field of study), direct feedback from scientific experts on the topic,

workshops (online or in person), videos, and support groups on social media. Given many

potential volunteers are eager to learn more (Martin, Smith, et al., 2016), these types of

educational opportunities may be reward enough to engage people with mid-range EiS

scores.

Only one PPSR role (data collection) received a positive mean score for the low EiS group,

yet this was barely over the scale midpoint. Despite this, the results show there are some

members of the low and ambivalent EiS groups who are willing to participate and donate a

great number of hours to assisting marine research. Compared to the higher EiS groups, these

lower two groups have a greater proportion of people who answered “maybe” to the question

about their willingness to help any type of scientific research. This hints at certain personal

caveats for volunteering. Matching the interests of these groups with the objectives of citizen

science project, and the roles volunteers can play, will be essential for engagement of these

groups. In addition, analysis of other data from this survey showed marine user groups (e.g.

recreational fishers or divers) are more willing to share data with some organizations than

others (Martin, Christidis, & Pecl, 2016). Relationships and trust are also essential for

engagement, which can be impacted by the worldview of the lead organization (Wynveen &

Sutton, 2015). Examples around the globe of community-driven, co-created (Shirk et al.,

2012) citizen science, such as the GardenRoots project (Ramirez-Andreotta, Brusseau,

Artiola, Maier, & Gandolfi, 2015), show that it is possible to engage a broad range of people

who are often brought together by a shared concern. These are the sorts of issues that need to

be considered in citizen science project design and communication for recruiting the lower

EiS groups.

The low EiS group reported much lower education levels than others, making them a key

group science communication strategies aspire to target (Cormick, 2012). Engaging these

audiences in citizen science may prove to be an impossible barrier for many scientist-led

projects. The situation is made more difficult by the majority of citizen science projects

asking the volunteers to come to them (rather than taking the science to the audience and

engaging on their terms) and demonstrates a lack of understanding about the disconnection

some groups have with the “elite” world of science (Callon, 1999).

Conclusion

Increasing the public’s understanding and support for science is not the main priority for

many citizen science projects (Bonney et al., 2015; Wiggins & Crowston, 2011), and nor

should it be for all projects, yet it is an often-stated aim in citizen science practice and is

promoted as such by governments keen to see greater public engagement in science

(Department of Industry, Innovation and Science [DIIS], 2016; Museum für Naturkunde,

2016; Pecl et al., 2015). The results presented here suggest mainstream scientist-led projects,

which form the majority of citizen science practice (Kullenberg & Kasperowski, 2016), may

appeal more to a narrower type of audience, that is, one which is already paying attention to,

and is supportive of science.

What does this mean for citizen science? In the big picture, these findings are important

considerations for the future direction of citizen science, which is guided by statements and

support from citizen science associations, governments, funding agencies, research

institutions, NGOs and the like. The actors at this level will need to think about how they can

support a greater diversity of participants in citizen science if broader science engagement is

the goal. At the individual project level, the aims of each will differ and improving public

science engagement may not be the priority for some. For those that aspire to achieve this,

these results demonstrate the need to understand who their target audience is and how best to

recruit and retain them.

For citizen science to reach its full potential in facilitating broader societal impact in public

science engagement, there are considerable institutional, practical, and societal barriers that

need to be removed. Challenges exist for all stakeholders in citizen science. Addressing these

barriers will require a rethinking of the way citizen science is designed, implemented,

supported, promoted, reported, and importantly, the way it incorporates the wider community

in its practice.

Acknowledgements & Funding

The author would like to thank the editors and reviewers who provided constructive feedback

and suggestions which greatly improved this work. Thanks must also go to all the

respondents who generously gave their time to this research. This study was conducted

during the author’s PhD in the School of Environment, Science & Engineering, SCU, at

which time she was supported by an APA scholarship from the Australian Government. The

writing of this article was funded through a short-term postdoctoral position in the School of

Education, SCU. The research was supported by an Australian Postgraduate Award from the

Australian Government, along with funds from the School of Environment, Science &

Engineering at Southern Cross University.

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Author Biography

Victoria Martin, PhD, is an environmental social scientist. Over the past two decades her re-search has focused on environmental impact and management issues in marine and terrestrial environments. Her interests now extend to science communication and public engagement in citizen science. From June 2017 she will continue her citizen science research with the Cor-nell Lab of Ornithology as a Rose Postdoctoral Research Fellow.

Table 1. Mean scores for EiS questions by EiS group

EiS question

Low EiS (N = 67)

Ambivalent EiS (N = 164)

Moderate EiS (N = 275)

High EiS(N = 267)

Very high EiS(N = 56)

Working scientists(N = 316)

M SD Min Max M SD Min Max M SD Min Max M SD Min Max M SD Min Max M SD Min Max

interest1 3.57 1.03 1 5 4.82 .72 3 7 5.77 .65 4 7 6.76 .44 5 7 7.00 .00 7 7 6.72 .64 2 7

seek2 2.55 .88 1 5 4.05 .78 2 6 5.13 .66 3 7 6.35 .61 5 7 7.00 .00 7 7 6.49 .84 2 7

understand3 3.10 1.24 1 5 4.30 .83 2 6 5.12 .75 3 7 5.93 .65 4 7 7.00 .00 7 7 6.01 .93 2 7Note: EiS = engagement in science. The variable trust was removed from the analysis. 1How interested are you in science, generally? 1 = not at all interested, 7 = extremely interested2In general, how often do you actively seek out scientific information? 1= never, 2 = very frequently (e.g. on a daily basis)3In general, how easy do you find it to understand scientific information? 1 = very difficult, 7 = very easy

Table 2. Description of EiS groupsTotal sample(N = 1145) Low EiS

(N = 67)Ambivalent EiS

(N = 164)Moderate EiS

(N = 275)High EiS(N = 267)

Very high EiS(N = 56)

Working scient-ists

(N = 316)

N M SD N % N % N % N % N % N %

Gender Female 517 17.24 3.28 25 37.3% 71 43.3% 101 36.7% 113 42.3% 22 39.3% 185 58.5%

Male 609 16.92 3.25 39 58.2% 90 54.9% 169 61.5% 146 54.7% 34 60.7% 131 41.5%Missing 19 (1.7%)

Age 15 to 19 years 43 16.67 3.08 2 3.0% 7 4.3% 15 5.5% 15 5.6% 4 7.1% 0 0.0%20 to 24 years 95 18.03 3.06 4 6.0% 4 2.4% 20 7.3% 22 8.2% 6 10.7% 39 12.3%25 to 34 years 235 17.54 3.20 12 17.9% 29 17.7% 50 18.2% 41 15.4% 13 23.2% 90 28.5%35 to 44 years 255 17.38 3.11 13 19.4% 27 16.5% 56 20.4% 63 23.6% 10 17.9% 86 27.2%45 to 54 years 252 16.87 3.30 14 20.9% 45 27.4% 55 20.0% 65 24.3% 12 21.4% 61 19.3%55 to 64 years 188 16.13 3.36 14 20.9% 42 25.6% 51 18.5% 38 14.2% 7 12.5% 36 11.4%65 to 74 years 53 16.43 3.47 5 7.5% 6 3.7% 21 7.6% 13 4.9% 4 7.1% 4 1.3%75 to 84 years 5 16.40 2.51 0 0.0% 1 0.6% 2 0.7% 2 0.7% 0 0.0% 0 0.0%Missing 19 (1.7%)

Higher education Postgraduate Degree 257 18.45 2.63 5 7.5% 16 9.8% 40 14.5% 46 17.2% 15 26.8% 135 42.7%Graduate Diploma/Graduate Certificate 109 17.60 3.10 3 4.5% 9 5.5% 23 8.4% 37 13.9% 3 5.4% 34 10.8%Bachelor Degree 311 17.72 2.83 7 10.4% 35 21.3% 63 22.9% 73 27.3% 21 37.5% 112 35.4%Advanced Diploma/Diploma 122 16.25 3.21 9 13.4% 28 17.1% 29 10.5% 37 13.9% 3 5.4% 16 5.1%Certificate (e.g. Trade Certificate) 254 15.31 3.48 33 49.3% 64 39.0% 87 31.6% 47 17.6% 12 21.4% 11 3.5%No higher education 72 16.29 3013 6 9.0% 9 5.5% 28 10.2% 19 7.1% 2 3.6% 8 2.5%Missing 20 (1.7%)

Highest level of education in sci-ence

Studied science after high school (i.e. uni/other) 574 18.86 2.08 3 4.5% 20 12.2% 69 25.1% 151 56.6% 42 75.0% 289 91.5%Elective/specific high school science subjects 237 16.26 2.85 14 20.9% 42 25.6% 91 33.1% 63 23.6% 10 17.9% 17 5.4%Compulsory/general high school science subjects 282 14.57 3.17 41 61.2% 87 53.0% 100 36.4% 43 16.1% 4 7.1% 7 2.2%Only studied science at primary school 16 13.38 3.42 3 4.5% 6 3.7% 5 1.8% 1 0.4% 0 0.0% 1 0.3%Never studied science before 16 13.44 4.40 2 3.0% 6 3.7% 5 1.8% 1 0.4% 0 0.0% 2 0.6%Missing 20 (1.7%)

Working in sci-ence

Currently working in science/science industry 316 19.22 2.06 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 316 100.0%No, but I used to work in science/the science industry 108 18.76 2.10 0 0.0% 6 3.7% 23 8.4% 58 21.7% 21 37.5% 0 0.0%

No, I have never worked in science/the science industry 700 15.85 3.22 63 94.0% 155 94.5% 247 89.8% 200 74.9% 35 62.5% 0 0.0%Missing 21 (1.8%)

Table 3. General interest in assisting research

Low EiS Ambivalent EiS Moderate EiS High EiS Very high EiS Working scientists

N % N % N % N % N % N %

Are you interested in helping scientific research in some way?

Yes, I am definitely interested 18 26.9% 80 48.8% 186 67.6% 229 85.8% 53 94.6% 286 90.5%

Maybe 48 71.6% 82 50.0% 87 31.6% 38 14.2% 3 5.4% 29 9.2%

No, I am definitely not interested 1 1.5% 2 1.2% 2 0.7% 0 0.0% 0 0.0% 1 0.3%

Hours willing to volunteer for marine research

None 9 13.4% 5 3.0% 9 3.3% 1 0.4% 0 0.0% 4 1.3%

1-2 hours per year 4 6.0% 6 3.7% 7 2.5% 3 1.1% 1 1.8% 6 1.9%

half a day per year 4 6.0% 14 8.5% 9 3.3% 6 2.2% 0 0.0% 11 3.5%

1 day per year 4 6.0% 12 7.3% 28 10.2% 25 9.4% 2 3.6% 42 13.3%

several days per year 22 32.8% 56 34.1% 80 29.1% 66 24.7% 10 17.9% 81 25.6%

7 days per year 8 11.9% 16 9.8% 26 9.5% 21 7.9% 5 8.9% 32 10.1%

about one day per month (12 days per year) 9 13.4% 41 25.0% 65 23.6% 59 22.1% 16 28.6% 63 19.9%

more than 12 days per year 7 10.4% 14 8.5% 51 18.5% 86 32.2% 22 39.3% 77 24.4%

Correlation Kruskal-Wallis

General participation questions*rho

Np

H df pM SD M SD M SD M SD M SD M SD

How interested would you be in participating in marine re-

search in some way? Scale: 1=Not at all interested: 7=Very

interested

.367

[.312, .418] 1145 <.01 195.82 5 <.01 4.49a 1.53 5.25b 1.45 5.75c 1.24 6.34d .93 6.61d .87 6.31d 1.01

How much would you enjoy helping marine scientists?

Scale: 1=Not at all: 7=Very much

.372

[.318, .423] 1145 <.01 199.45 5 <.01 4.72a 1.52 5.49b 1.31 5.88c 1.13 6.45d .80 6.70d .81 6.44d .83

How confident do you feel in your ability to help marine re-

search? Scale: 1=Not at all confident: 7=Very confident

.424

[.372, .474] 1145 <.01 231.53 5 <.01 3.78a 1.48 4.62b 1.44 5.12c 1.29 5.75d 1.08 6.32e .94 5.95d,e 1.18* Notes: Correlations: BCa bootstrap 95% CIs reported in brackets. Bootstrap results are based on 1000 bootstrap samples. Means: Values in the same row and subtable not sharing the same subscript are significantly different at p< .05 in the two-sided test of equality for column means. Cells with no subscript are not included in the test. Tests assume equal variances.1 1Tests are adjusted for all pairwise comparisons within a row of each innermost subtable using the Bonferroni correction.

Table 4. Likelihood and correlations for specific PPSR activities

Correlation* Kruskal-Wallis test Low EiS**

Ambivalent

EiS** Moderate EiS** High EiS** Very high EiS**

Working

scientists**

rho N p H df p M SD M SD M SD M SD M SD M SD

Public debates about marine science .288

[.237,.339] 1145 <.01 125.48 5 <.012.60a 1.56 3.53b 1.74 4.05c 1.68 4.63d 1.74 5.18d 1.70 4.64d 1.81

Helping to process information (data) .282

[.229, .338] 1145 <.01 108.59 5 <.013.54a 1.81 4.19a,b 1.65 4.59b 1.50 5.12c 1.46 5.54c 1.75 5.19c 1.56

Helping to communicate the findings .313

[.265, .365] 1145 <.01 121.11 5 <.013.73a 1.86 4.46b 1.72 4.75b 1.57 5.27c 1.49 5.71c 1.42 5.54c 1.51

Helping plan individual marine research projects .306

[.251, .359] 1145 <.01 125.80 5 <.013.45a 1.66 4.12a,b 1.67 4.46b 1.65 5.05c 1.72 5.66c 1.53 5.21c 1.67

Helping to decide where funding and other resources

should be spent

.225

[.172, .281] 1145 <.01 72.97 5 <.013.33a 1.79 3.98a,b 1.71 4.17b 1.72 4.66c 1.75 5.29c 1.76 4.77c 1.76

Collecting information (data) for marine scientists .245

[.190, .300] 1145 <.01 90.47 5 <.014.88a 1.67 5.38a 1.30 5.84b 1.11 6.03b 1.15 6.34b 1.16 6.03b 1.25

Helping to analyse the findings .305

[.251, .354] 1145 <.01 128.36 5 <.013.72a 1.71 4.31a 1.55 4.87b 1.50 5.26b,c 1.58 5.86c 1.43 5.39c,d 1.58

Helping to decide what topics marine research should

focus on in the future

.278

[.227, .330] 1145 <.01 105.39 5 <.013.85a 1.64 4.37a,b 1.57 4.70b 1.50 5.21c 1.58 5.59c 1.59 5.30c 1.52

Acting as a representative to explain the concerns

that society has about marine research

.275

[.222, .332] 1145 <.01 100.72 5 <.013.27a 1.84 3.93a,b 1.74 4.29b 1.72 4.95c 1.74 5.16c 1.65 5.00c 1.78

*Notes: Correlations: BCa bootstrap 95% CIs reported in brackets. Bootstrap results are based on 1000 bootstrap samples. **Means: Values in the same row and subtable not sharing the same subscript are significantly different at p< .05 in the two-sided test of equality for column means. Cells with no subscript are not included in the test. Tests assume equal variances.1 1Tests are adjusted for all pairwise comparisons within a row of each innermost subtable using the Bonferroni correction.