Rethinking 2S/LGBTQI+ food security with co-design: a study protocol
Bibliographic record
Abstract
Context: In Canada, recent statistics show that 8.7 million Canadians face food insecurity which disproportionately affects people of the 2S/LGBTQI+ communities. Food insecurity is intersectional: people belonging to one or more marginalized groups, like 2S/LGBTQI+, are at greater risk. Moreover, food security resources can pose due to the stigma and cis-heterosexism associated with the religious basis of some of these resources. Exploring ways to partner up with and for 2S/LGBTQI+ communities and food security organizations in order to reflect and imagine a new service model is a promising avenue to tackle the social injustice of food insecurity. Objectives: This research protocol presents the activities and strategies of a co-design study aiming to enhance safety and inclusivity of food security services with and for 2S/LGBTQI+ individuals. The team also seeks to identify how to improve food security services with and for 2S/LGBTQI+ communities and to co-create a prototype service model representing safe and inclusive services that communities and food security stakeholders can utilize to make improvements in that direction. Methods: This protocol is based on a co-design methodology inspired by design thinking. The project will address desirability, feasibility, and viability - what is desirable, acceptable, achievable and sustainable in a prototype service model for 2S/LGBTQI+ individuals accessing food security services, organizations, and workers/volunteers. Participants will take part in seven online co-design workshops. Facilitators will guide the participants in offering free commentary, generating thoughts, and sharing new ideas along with reflective questions regarding a provisional prototype of the service model and framework principles. Discussions will be recorded for analysis purposes along with visual and textual content generated through the web-based collaborative tool. The data will be subjected to a qualitative thematic analysis. Conclusion: This protocol recognizes and values the experience and knowledge of 2S/LGBTQI+ communities and illustrates participatory involvement to improve food security. It is expected that this protocol inspires researchers and organizations to partner up and explore ways to use, replicate, and improve or adapt the approach. Future results may find interest and usefulness in other 2S/LGBTQI+ communities and food security organizations.
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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 | 0.167 | 0.167 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.041 | 0.011 |
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".