Differences between key workers: IFS Briefing Note BN285
Bibliographic record
Abstract
Key findingsKey workers as a whole are a cross-section of the UK workforce: in terms of their age, their education and where they were born, key workers look similar to the rest of the workforce.However, they are more likely to be female and are somewhat lower-paid than other employees: the median key worker earned £12.26 per hour in today's prices last year, 8% less than the £13.26 per hour earned by the median earner in a non-key occupation.But there are big differences between key workers in different sectors.The food and social care sectors stand out for the low wages their employees earn and the low levels of qualifications their workers hold.Older, self-employed farmers mean that nearly a sixth of food sector workers are aged 65 or over.Younger, migrant food processors mean that 30% of workers in the sector were born somewhere other than the UK, as were a quarter of health and social care workers.These differences translate into significant variation in key worker wages: the median earner in the food sector earned £8.59 per hour last year, 30% less than the median key worker.But the median earner in key professional servicessuch as justice or journalism -earned more than half as much again as the average key worker, partly reflecting that nearly 80% have degrees.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.378 | 0.160 |
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".