Organizing in a Global City Priority #7: Build a Multi-Racial and Inclusive Labour Movement
Bibliographic record
Abstract
economic literacy for trade unionists and other activists. We used Stanford’s book as a guide, but went far beyond it – with additional topics, reading material, and guest speakers. By the end of our course we had 30 confident, capable activists who felt a lot better about taking on economic debates and talking to their co-workers and neighbours about economic issues and alternatives. As one participant put it, “I’ve been waiting to have this conversation for 20 years.” That alone won’t change the world, of course. But if we do more of it, we’ll be better prepared ourselves to help change the world. Our participants found the discussion both interesting and useful. Indeed workers take to it instinctually. After all, when they realize what real economics is actually about – their daily lives – they understand that they already know a lot about it. I think of political-economy training as a kind of “road map” for labour activists and socialists. Like any map, we need it for three things: to figure out where we are, where we want to go, and how to get there. I would like to see all unions, local labour councils and labour centrals step up their efforts on this front. We need to equip our leadership and activist base with a stronger critique of the current situation, and to arm them as citizens with a better understanding of the alternatives we can and must be fighting to win.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".