Toward Food Sovereignty for Coastal Communities of Eastern Quebec: Co-Designing A Website to Support Consumption of Edible Resources from the St. Lawrence
Bibliographic record
Abstract
Background: Despite the abundance and proximity of edible marine resources, coastal communities along the St. Lawrence in Eastern Quebec rarely consume these resources. Within a community-based food sovereignty project, Manger notre Saint-Laurent (Sustenance from our St. Lawrence), members of participating communities (three non-Indigenous, one Indigenous) identified a need for a web-based decision tool to help make informed consumption choices.Objective: 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.Method: We co-designed a prototype with community members, regional stakeholders, and experts in user experience design and web development. We conducted 48 interviews with a variety of people over three 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; for example, confusing symbols. We resolved issues and added features people identified as useful. The final prototype’s usability was rated at “best imaginable” with scores similar across socio- demographic groups.Conclusion: By co-designing with community members, regional stakeholders, and other experts from the beginning, we were able to integrate communities’ priorities and perspectives about edible marine resources into a prototype website adapted to community members' needs. The final prototype includes a tool to explore species, and index cards to regroup accurate evidence about food safety, nutrition, sustainability, regional and seasonal availability, taste properties, and responsible fishing, hunting, picking, and preparation methods.
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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 | 0.004 | 0.005 |
| 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 | 0.003 | 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".