Organizing Working-Class Communities: Lessons from POWER'S Experiences
Bibliographic record
Abstract
I want to thank the Socialist Project, and in particular Leo Panitch and Sam Gindin, for making it possible for me to be here. I also want to thank the Canadian people for allowing me to be in a country where I can proclaim myself a socialist and not have to fear that I will end up on Fox News. People Organized to Win Employment Rights’ (POWER’s)1 mission is to eradicate poverty and oppression once and for all. When we wrote that mission statement in 1997, we were very clear about its implications. Back then, we were seeing an increase in the level of structural unemployment, which forced more and more people out of working-class industries that previously had allowed them to make ends meet, to raise families, and to thrive in urban communities. More and more people were winding up homeless on the streets. More and more people were winding up without health insurance. More and more poor people were being criminalized and vilified for being poor as a result of capitalist accumulation, but in the face of these injustices, there was silence from the centres of power in our country. We felt it was critical, absolutely critical, for working-class folks, low-income people of colour, to have a space to be able to weigh in on the public policy decisions that were affecting their lives, so five welfare recipients and I went about building such an organization.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.119 | 0.077 |
| Scholarly communication | 0.022 | 0.012 |
| Open science | 0.006 | 0.023 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 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 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".