Evaluation of a national citizen science programme for public benefit: Engagement in the citizen science of Soilsafe Aotearoa
Bibliographic record
Abstract
Aotearoa New Zealand’s history of soil contamination combined with its culture of home gardening has the potential to put New Zealanders at risk of exposure to trace metal contaminants from their backyards. Soilsafe Aotearoa (SSA) is a New Zealand-based citizen science (CS) programme that examines this risk, screening for concentrations of trace contaminants in participants’ domestic soils. CS programmes generally promise to make science more democratic by narrowing the gap between science and the public; SSA does this by returning heavy metals report of each participant’s soil screening back to them along with interpretive data and guidance of what to do next. \nThis thesis explores how participants engaged with this citizen science programme and the impact of their engagement by looking at the demographics of the citizen scientists, their motivations for engagement, and the outcomes of their engagement. Data was collected through an online survey sent out to previous SSA participants who had their soils screened and had received their results (n=855 at that time). Respondents (n=161) were mainly women of European descent who had received at least an undergraduate degree, who were part of wealthier households, and who owned their homes. Motivations for engagement typically revolved around their concerns for the safety of garden food production. Respondents felt that the biggest gains in knowledge from participating were in learning about their own soils from the results they had received from SSA, though only about a quarter of respondents took action to remediate any issues, such as through soil remediation, changing the plants growing in their garden, or learning more about the problem and potential solutions. Factors such as cost, feasibility, pre-existing knowledge, and level of risk determined the actions that respondents did or did not take. A large majority of respondents found SSA’s free soil metal screening service to be useful and would recommend it to others. Recommended further research directions include engaging with underrepresented groups (e.g., Māori, Pasifika, youth, renters) and exploring more deeply the soil values of participants through methods such as interviews or focus group discussions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".