Environmental victims and climate change activists
Bibliographic record
Abstract
There are powerful examples of victims of historical and ongoing direct harms gathering forces to fight for justice and reparations: from native peoples in Canada and Australia demanding redress for harms suffered since colonial times (Cunneen and Tauri 2016; Jung 2009) to current movements against gender-based violence or racialized police brutality such as #MeToo and #BlackLivesMatter. Overcoming individual agony and organising resistance is extremely difficult in cases such as these.<p></p> Yet even more challenges appear when struggling against, allegedly, more abstract and indirect harms such as climate change.<sup>1</sup> Ultimately, we are all affected by environmental harms, and particularly by climate change (Hall 2014; Watts 2018). Moreover, our own humane existence is under threat (White 2019; Sanchez-Bayo and Wyckhuys 2019). However, the impact of climate change is more diffuse, immaterial and abstract than crimes, such as murder or rape, and this peculiarity is what might make commitment to resistance even more challenging than usual.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".