Citizen science in monitoring food environments: a qualitative collective case study of stakeholders’ experiences during the Local Environment Action on Food project in Alberta, Canada
Bibliographic record
Abstract
BACKGROUND: Citizen science bears potential to build a comprehensive view of global food environments and create a broader discussion about how to improve them. Despite its potential, citizen science has not been fully utilised in food environment research. Thus, we sought to explore stakeholders' experiences of the Local Environment Action on Food (LEAF) project, a community-based intervention that employs a citizen science approach to monitoring food environments. METHODS: We used a qualitative collective case study design to explore citizen science through the LEAF process in seven communities in Alberta, Canada. Data generating strategies included semi-structured interviews with citizen scientists (n = 26), document review of communities' Mini Nutrition Report Cards (n = 7), and researcher observation. Data were analyzed in a multi-phase process, using Charmaz's constant comparison analysis strategy. RESULTS: Analysis revealed two main themes: relationship building and process factors. Communities used three interconnected strategies, engaging the right people, treading lightly, and reaching a consensus, to navigate the vital but challenging relationship building process. Process factors, which were influences on the LEAF process and relationship building, included the local context, flexibility in the LEAF process, and turnover among LEAF community groups. CONCLUSION: Citizen science through the LEAF project supported the creation and application of food environment evidence: it enabled residents to collect and interpret local food environment data, develop realistic recommendations for change, and provided them with an evidence-based advocacy tool to support the implementation of these recommendations. We recommend a web application that enables independent community food environment assessments. Such a tool could stimulate and sustain citizen involvement in food environment efforts, helping to build the necessary evidence base and promote the creation of healthy food environments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.035 | 0.013 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".