Climate Change, Work and Workers: A Bibliography
Bibliographic record
Abstract
This bibliography was created with Zotero software, and consists of over 4000 references to books, journal articles, working papers and reports – with an emphasis on Canadian authors and experience. It is organized by topics, such as Just Transition, Environmental Racism, and Labour union documents. It is also searchable by key words, author, title, and allows users to select items of interest and construct their own bibliographies. \n \nThe database may still be available at https://www.zotero.org/w3citations/library, but has not been maintained or updated since December 2021. \n \nBibliographies derived from the whole database are: Just Transition; Climate initiatives by Canadian labour unions; Environmental Racism. \n \nThe CSV export from Zotero can be used by those with the Zotero application installed on their computers. To download the free Zotero application, go to https://www.zotero.org/. Download this CSV file to your hard drive, open the Zotero application, and use the “Import” function in Zotero to view the contents. \n \nAn alternative for those with Zotero on their computers: use the compressed folder and move it directly into the Zotero user directory. It consists of an SQL file and a "storage" folder of library items.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.020 | 0.045 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.006 |
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".