Climate Justice Partnership Linking Universities and Community Organizations in Toronto, Durban, Maputo and Nairobi
Bibliographic record
Abstract
This paper describes a project based at York University in Toronto, funded through the Climate Change Adaptation in Africa program of the International Development Research Centre and the UK Department for International Development (DFID), which is working to increase the participation of marginalized groups, especially women, in urban water governance.Students and faculty members from the University of Nairobi, Kenya; Eduardo Mondlane University in Maputo, Mozambique; and the University of KwaZulu-Natal in Durban, South Africa are working with civil society organizations in the three cities and with York University researchers to show how organizing in local communities can help the vulnerable to deal with climate change.As people in marginalized communities begin to address collectively the impacts of climate change, this summons political attention and allows those with direct experience to influence government policy. Civil society organizations, with support from local and international faculty and students, facilitate and focus this activism. University students help to document the NGOs’ work during internships with the NGOs. They also learn community development skills and make contacts. Faculty members publish and disseminate ideas about grassroots climate change adaptation and resulting political responses through presentations, publications and the project’s website (www.ccaa.irisyorku.ca)
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.013 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".