Green Collective Agreements database
Bibliographic record
Abstract
The Green Collective Agreements database identifies almost 300 green clauses which reflect the ways in which Canadian labour unions have sought to protect their members’ health, safety, job security, or pay, and to discharge their broader social responsibility to mitigate climate change impacts. The database was compiled by searching the publicly available websites such as the federal government’s Negotech website (https://negotech.labour.gc.ca/cgi-bin/RechercheSearchNegotheque/index.aspx), as well as provincial websites. Key unions also provided texts of their agreements. \n \nThe ACW Green Collective Agreements searchable database may still be available at https://www.zotero.org/green_agreements/library, but has not been updated since December 2021. \n \nGreen Bargaining Language 2022 Samples from the ACW Database.pdf is provided as an easy-to-read sampling of agreements and the text of clauses. \n \nThe GreenCollectiveAgreements20220316.csv file 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/. Then download the CSV file to your hard drive, open the Zotero application, and use the “Import” function in Zotero to save 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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.024 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.259 | 0.221 |
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".