Moving In and Out of In-work Poverty in the UK: An Analysis of Transitions, Trajectories and Trigger Events
Bibliographic record
Abstract
Abstract There is growing concern about the problem of in-work poverty in the UK. Despite this, the literature on in-work poverty remains small in comparison with that on low pay and, in particular, we know relatively little about how people move in and out of in-work poverty. This paper presents an analysis of in-work poverty transitions in the UK, and extends the literature in this field in a number of identified ways. The paper finds that in-work poverty is more transitory than poverty amongst working-age adults more generally, and that the number of workers in the household is a particularly strong predictor of in-work poverty transitions. For most, in-work poverty is a temporary phenomenon, and most exits are by exiting poverty while remaining in work. However, our study finds that respondents who experience in-work poverty are three times more likely than non-poor workers to become workless, while one-quarter of respondents in workless, poor families who gained work entered in-work poverty. These findings demonstrate the limits to which work provides a route out of poverty, and points to the importance of trying to support positive transitions while minimising negative shocks faced by working poor families.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".