Bibliographic record
Abstract
The following publication considers the different labour market experiences of women and men in Northern Ireland.A consistent feature of the labour market is higher employment and unemployment rates for males and higher inactivity rates for females.These features are explored using estimates from the Labour Force Survey quarterly and household datasets. Key Points: The employment rate for males in NI has been consistently higher than for females over the past ten years.Although the number of employees in NI was evenly split between males and females in 2019, the number of self-employed males was more than double the number of self-employed females. Males were more likely to work full-time than females.Furthermore, approximately 60% of employed women with dependent children worked full-time, compared to 95% of employed males with dependent children. The unemployment rate for males in NI has been consistently higher than for females over the past ten years, however, the gap is narrowing between the two.In 2019, 44% of the unemployed were female and 56% were male. Over the past 10 years there have been consistently more economically inactive women than men.In 2019, just under a third of working age women were economically inactive, compared to just under a quarter of men. The most common reason for inactivity among women was family and home commitments, while the most common reason for men was sickness or disability.The difference in inactivity rates between men and women can be entirely accounted for by the number inactive due to looking after family/home.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.075 | 0.039 |
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".