Crafting a Statutory Union Recognition Procedure that Works for the UK
Bibliographic record
Abstract
At least 700 Union recognition agreements were signed in the UK between 2001 and 2002, according to the records of the Trade Union Congress (TUC). This contrasts starkly with the 1990s, when throughout the whole decade the number was less than 100 per year. The signs of a reversal of fortunes for the trade unions became apparent in 2000 when the TUC recorded around 150 new agreements. This rise in voluntary recognitions suggests that the Labour Government’s statutory recognition procedure, introduced under the Employment Relations Act in 2000 (ERA), is stimulating recognitions as the trade unions had hoped it would. The Department of Trade and Industry (2003: 28) in its Consultative Document on the Review of the Employment Act, which it published in February 2003, in fact used the evidence of a general rise in voluntary recognition agreements as the main support for its claim that ‘the procedure is, overall, working well’. In this chapter we review the operation of the system in order to assess this conclusion. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.012 |
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".