The Datafied Workplace and Trade Unions in the UK
Bibliographic record
Abstract
From concerns about job losses to increased surveillance and work intensification to the creation of novel forms of gig work and platform labour, the advancement in data-driven technologies is now a key part of the future of work, working conditions and workers' rights. Trade unions are central to this discussion, but it is not always clear how they understand and engage with these developments. This working paper sets out a brief overview of how trade unions in the UK understand the challenges of the datafied workplace and how they are responding to them. It is based on interviews with officials from 15 different trade unions in the UK carried out during 2021, and forms part of a larger project on the social justice implications of datafication1 . For simplicity, we have structured our findings according to three themes for each section that highlight the dominant responses we received in our interviews
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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 | 0.016 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.024 | 0.023 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.004 | 0.005 |
| 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".