The Data Practices of Grassroots Climate Justice Groups in Toronto: Exploring Data-Driven Tools for Organizing
Bibliographic record
Abstract
This thesis examines data practices within Toronto-based climate justice groups. In this context, data practices refer to strategies involving interaction with, or (co)production of data to support climate justice organizing. The central research question is: in what ways do Toronto-based grassroots climate justice groups engage with data practices to further their goals? A theoretical framework of environmental data justice is employed to guide this project. A qualitative research design rooted in reflexive thematic analysis is utilized for this project, with methods including an analysis of digital content produced by grassroots climate justice groups and a series of semi-structured interviews. The findings of this research project determine that there are multiple ways that groups engage with data practices to support their organizing efforts. The purpose of this project is to understand how data practices are applicable to grassroots climate justice organizers and the barriers they face when engaging with climate data.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.020 | 0.020 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| 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".