Bibliographic record
Abstract
The authors thank the Leverhulme Foundation for financial support. LFS data made available by the ONS through the Data Archive at the University of Essex. The views expressed are those of the authors and do not necessarily reflect those of the Leverhulme Trust. Thanks to Marco Manacorda, Alan Manning and John Schmitt for helpful comments and suggestions. Thanks also to Kirstine Hansen for Individual and household based aggregate measures of joblessness can, and do, offer conflicting signals about labour market performance. This paper introduces a simple set of indices which can be used to measure joblessness at the household level and which can be used to try to identify the likely source of any disparity between individual and household-based measures of worklessness. We focus on one measure that can be decomposed in order to isolate the source of any discrepancy. Built around a comparison of the actual household jobless rate with that which would occur if work were randomly distributed over the working age population, we show that in Britain and in certain other OECD countries there has been a growing disparity between the individual and household based jobless measures, which we term polarisation. Changing household size in Britain can only account for a quarter of the rise in polarisation so that differences between individual and household jobless performance
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.030 |
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; both teacher heads agree on what is shown here.
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".