Innovations from the Margins: Creating Inclusive and Equitable Academic-Community Research Collaborations
Bibliographic record
Abstract
How does one build a Request for Proposals (RFP) process that allows for bottom-up participation while simultaneously being pragmatic and adept enough to manoeuvre the complexities of a multi-stakeholder environment defined by differing interests, objectives, mandates, and power dynamics? This article showcases the findings from participatory work with stakeholder groups working in the area of food security in Southern Ontario’s Halton Region. It demonstrates a process designed with the specific intent of increasing the engagement of beneficiaries and service providers in the RFP process. Finally, the article seeks to shed additional light on theory and practice of “participatory approaches” in the context of philanthropy. It is important to be realistic in not reifying participation itself in this context. In both theory and practice, this means adopting lenses and models that openly consider the complex realities, political obstacles, and trade-offs that occur when negotiating participation in this environment.RÉSUMÉCet article aborde la question suivante: comment créer un processus de demande de propositions (DP) permettant une participation ascendante tout en étant suffisamment pragmatique et suffisamment habile pour gérer les complexités d’un environnement multipartite défini par des intérêts, objectifs, mandats et dynamiques de pouvoir différents? La question est répondue en présentant les résultats d’un projet de travail participatif intégrant des intervenants travaillant dans le domaine de la sécurité alimentaire dans la région de Halton, dans le sud de l’Ontario. L’article illustre un processus conçu qui a le but spécifique d’accroître la participation des bénéficiaires et des fournisseurs de services au processus de demande de propositions. Enfin, l’article cherche à apporter une réflexion additionnelle sur la théorie et la pratique des « approches participatives » dans le contexte de la philanthropie. Il met de l’avant l’importance d’être réaliste dans ses attentes pour ne pas réifier les bienfaits de la participation dans ce contexte. En théorie et en pratique, cela signifie d’adopter des objectifs et des modèles qui tiennent compte ouvertement des réalités complexes, des obstacles politiques et des compromis qui se produisent lors de la négociation de la participation un tel environnement.
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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 | 0.177 | 0.162 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.016 | 0.040 |
| Scholarly communication | 0.033 | 0.033 |
| Open science | 0.006 | 0.064 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".