Data Tactics in Worker Advocacy Research
Bibliographic record
Abstract
This paper draws on Michel de Certeau's notion of ''tactics'' to explore the use of data in labor organizing research in CSCW. Taking a historical view, we first analyze a set of cases from 20th century US labor history that offer three distinct lenses on the risks of data-based advocacy campaigns: wagers, compromises, and concessions. Across our cases, we frame reformers' use of data tactics as a rhetorical move, taken to advance incremental worker gains under conditions of precarity. However, by continuing to rely on certain data-based arguments in the short term, we argue that labor reformers may have limited the frame of debate for broader arguments necessary to improve conditions in the long-term. These tensions follow us into data-based advocacy research in the present, such as the emerging ''digital workerism'' movement. To ensure the continuation of responsible advocacy research in CSCW, we offer insights from social justice movements to suggest how members of the HCI and CSCW communities can work more intentionally alongside (or without) data methods to support worker-led direct action.
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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 | 0.122 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.017 | 0.089 |
| Scholarly communication | 0.025 | 0.031 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".