Moving Towards Greater Justice: A Community-Based Research Project on Transit Affordability in Toronto
Bibliographic record
Abstract
This report reflects on a collaborative advocacy research project I undertook in partnership with the Fair Fare Coalition (FFC), a transit activist and advocacy organization in \nToronto. The project is a community-based research project on transit affordability involving the participation of low-income Torontonians throughout the city. The purpose was to bring together voices that are usually excluded from official city planning discourses and decision-making processes to highlight some of the frequently unaccounted for "costs" of increasing transit fares in Toronto – for example, on individual and community health and well-being. Through this, the Fair Fare Coalition hoped to build capacity and mobilize knowledge towards advocating for policy measures to increase transit affordability in Toronto. \n \nThe participatory project's goals and outcomes are twofold. One goal is for the participatory process to culminate in a project deliverable that could be used for advocacy \npurposes in support of the Fair Fare Coalition's advocacy goals. The second goal is increasing community knowledge-building and mobilization, including ongoing political and advocacy actions. This is to highlight the fact that both the process and outcome are valuable and important. \n \nFor the purposes of this report, I will contextualize and situate the significance of the FFC project in Toronto, providing background, exploring relevant literature, and explaining the \nimportance of the research methodology. I will then share brief findings from the research, and provide analysis of both the outcomes and process of the research project.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.027 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".