Strength in Networks: Employment Rights Organizations and the Problem of Co‐Ordination
Bibliographic record
Abstract
Abstract In recent decades, alternative organizations and movements —‘quasi‐unions’— have emerged to fill gaps in the US system of representation caused by union decline. We examine the record of quasi‐unions and find that although they have sometimes helped workers who lack other means of representation, they have significant limitations and are unlikely to replace unions as the primary means of representation. But networks, consisting of sets of diverse actors including unions and quasi‐unions, are more promising. They have already shown power in specific campaigns, but they have yet to do so for more sustained strategies. By looking at analogous cases, we identify institutional bases for sustained networks, including shared information platforms, behavioural norms, common mission and governance mechanisms that go well beyond what now exists in labour alliances and campaigns. There are substantial resistances to these network institutions because of the history of fragmentation and autonomy among both unions and quasi‐unions; yet we also identify positive potential for network formation.
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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 | 0.014 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.039 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".