Organizing Equality: Crises, Contexts, and Possibilities
Bibliographic record
Abstract
Income inequality deepens around the globe; unemployment and low-waged precarious jobs are on the rise as traditional factory jobs shutter or relocate to the Global South and the new gig economy solidifies; extractive industries proliferate in the face of imminent climate disaster; and government funding for public goods, such as health care and education, are hitting record lows in Western nations as austerity logics prevail.In response, Indigenous-rights movements and the Black Lives Matter movement emerge and strengthen, and pro-democracy and anti-austerity movements spread from Tahir Square to Ghezi Park, from Athens to London, from Montreal to Wall Street.In 2017, these intensifying and intersecting sets of global, national, and local challenges inspired our desire to convene a conference, entitled Organizing Equality, which would tackle the pressing need to mobilize across identities, social location, abilities, and politics to address issues of economic and social inequality.Sponsored by the Faculty of Information and Media Studies at Western University and held in London, Ontario, Canada, we hoped the conference would facilitate meaningful connections between the many disparate global struggles for social, economic, and environmental justice, and would give voice to our own personal and professional frustrations with the neoliberal austerity stranglehold on our federal and local governments, media, and universities.Our goal was modest when we began, but interest in the conference far exceeded our expectations.We received proposals from a wide variety of activists, artists, and
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.055 |
| Scholarly communication | 0.019 | 0.017 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".