Toward Food Sovereignty for Coastal Communities of Eastern Québec: Co-designing A Website to Support Consumption of Edible Resources from the St. Lawrence River, Estuary, and Gulf
Bibliographic record
Abstract
Background. Despite the abundance and proximity of edible marine resources, coastal communities along the St. Lawrence in Eastern Québec rarely consume these resources. Within a community-based food sovereignty project, Manger notre Saint-Laurent (“Sustenance from our St. Lawrence”), members of participating communities (3 non-Indigenous, 1 Indigenous) identified a need for a web-based decision tool to help make informed consumption choices. Methods. We thus aimed to co-design a prototype website that facilitates informed choices about consuming local edible marine resources based on seasonal and regional availability, food safety, nutrition, and sustainability, with community members, regional stakeholders, and experts in user experience design and web development. We conducted 48 interviews with a variety of people over 3 iterative cycles, assessing the prototype’s ease of use with a validated measure, the System Usability Scale. Results. Community members, regional stakeholders, and other experts identified problematic elements in initial versions of the website (e.g., confusing symbols). We resolved issues and added features people identified as useful. Usability scores reached “best imaginable” for both the second and the third versions and did not differ significantly between sociodemographic groups. The final prototype includes a tool to explore each species and index cards to regroup accurate evidence relevant to each species. Conclusions. Engaging co-designers with different sociodemographic characteristics brought together a variety of perspectives. Several components would not have been included without co-designers’ input; other components were greatly improved thanks to their feedback. Co-design approaches in research and intervention development are preferable to foster the inclusion of a variety of people. Once the prototype is programmed and available online, we hope to evaluate the website to determine its effects on food choices.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".