Skills and work organisation in Britain: a quarter century of change
Bibliographic record
Abstract
Abstract This paper overviews key findings concerning the evolution of job skill requirements in Britain, and their relationship to technology and work organisation, based on surveys dating from 1986. The use of skills has been rising, as indicated by several indicators covering multiple domains. Technological change is robustly implicated in these rises, but it is not possible to satisfactorily classify most tasks according to how easily they are encoded and thereby clearly link the changes to the nuanced theory of skill-biased technical change associated with asymmetric employment polarisation. Moreover, changing work organisation also contributes to explaining the rises, both in skills use and in skills development. Nevertheless, the extent of worker autonomy in the workplace declined notably during the 1990s; this decline is not accounted for by the data, but is thought to be associated with changing management culture. Changing skill requirements also affect pay. In addition to the education level both computing skills and influence skills attract a premium in the labour market. There is an increasing cost in terms of pay from overeducation and a rising prevalence of overeducation. Together, these changes are reflected in an increased dispersion of the graduate pay premium. While these findings have provided important contextual information for the development of skills policies, they have had little effect on engendering policies for stimulating improved job design.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".