PMP Stands for “Politicize, Mobilize, and Power” Priority #8: Integrate Unemployed Workers (Union and Non-Union) into the Fightback
Bibliographic record
Abstract
in something more than just a one-day event. The outreach for the Summit involved presentations and discussions in scores of meetings, and a dozen different languages. The Declaration on Good Jobs for All evolved from those interactions, and went through numerous amendments. The Summit was held on 22 November 2008. People who had never been in the same room before exchanged ideas and shared a determination to work together for a society that we could all be proud of. Presenters posed hard questions, and workshops buzzed. In the closing, Summit co-ordinator Judy Vashti Persad captured the spirit of the day with one word – magic. The Good Jobs for All Coalition has continued to develop – planning joint campaigns and supporting each other’s efforts. The Coalition is holding rallies to fight for improvements in Employment Insurance, supporting new regulations on temp agency work, demanding investment in social infrastructure, and advocating for a green economy with good local jobs. This coalition will no doubt face many challenges. But it represents an authentic expression of the changing working class in Toronto, and just may become a new model of community/labour organizing in the 21st century. The demographic reality is that the clear majority of the future working class will come from communities of immigrants, aboriginal, and racialized workers. The labour movement must root itself, authentically and powerfully, in these communities if we are to have a base that is able to defend past gains and fight for new victories.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.084 | 0.048 |
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".