Darker Politics: Democracies, Labour Rights and Climate Change - Poster
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The poster summarizes an ACW public event held on Friday, May 26 2017, at Innis Town Hall Theatre, University of Toronto, Toronto. Speakers consider the impact of the election of U.S. President Donald Trump on Canada and climate change policy. Specifically, speakers include: Larry Brown, President, National Union of Public and General Employees, moderator, introducing the event; Tony Burman, Distinguished Visiting Professor of Journalism at Ryerson University, “Democracy Under Threat”; Dr. Elaine Bernard, Senior Research Associate at the Labor and Worklife Program, Harvard Law School, “Trump’s War on Workers and the Environment” ; and Jim Chorostecki, Executive Director at B.C. Federation of Labour, “The Softwood Lumber Dispute is the Hatfields vs. McCoys Feud Without the Guns, So Far.” A recording of the event is available on YouTube at https://www.youtube.com/watch?v=im0Cv53fV_Y.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it