Climate Justice and Participatory Research
Bibliographic record
Abstract
Climate catastrophe throws into stark relief the extreme, life-threatening inequalities that affect millions of lives worldwide. The poorest and most marginalized, who are least responsible for the consumption and emissions that create climate change, are the first and hardest impacted, and the least able to protect themselves. Climate justice is simultaneously a movement, an academic field, an organizing principle, and a political demand. Building climate justice is a matter of life and death. Climate Justice and Participatory Research offers ideas and inspiration for climate justice through the creation of research, knowledge, and livelihood commons and community-based climate resilience. It brings together articulations of the what, why, and how of climate justice through the voices of energetic and motivated scholar-activists who are building alliances across Latin America, Africa, and Canada. Exemplifying socio-ecological transformation through equitable public engagement, these scholars, climate activists, community educators, and teachers come together to share their stories of participatory research and collective action. Grounded in experience and processes that are currently underway, Climate Justice and Participatory Research explores the value of common assets, collective action, environmental protection, and equitable partnerships between local community experts and academic allies. It demonstrates the negative effects of climate-related actions that run roughshod over local communities’ interests and wellbeing, and acknowledges the myriad challenges of participatory research. This is a work committed to the practical work of transforming socio-economies from situations of vulnerability to collective wellbeing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.192 | 0.146 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.026 | 0.087 |
| Scholarly communication | 0.021 | 0.012 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".