Belgium Country Report for the European Centre of Expertise (ECE) in the field of labour law, employment and labour market policy
Bibliographic record
Abstract
The recent edition of the Labour Force Survey (LFS) in Belgium shows an increase in the employment rate for 2016; however, it is only to men that this increase applies, while for women it remains the same. What this suggests is that the gender employment gap has reached a tipping point. The question remains as to whether this is correct, and if so, what are the drivers? This slight trend shift is more pronounced in the Flemish Region. Further examination of the annual figures reveals for women in the Flemish Region a very strong 1st quarter of 2015 (69,4%). And this pushes the annual figure for 2015 quite upwards. Idem dito for the trend levels (average of the last four quarters) of the quarters 2015-I to 2015-IV. After having checked several sources one may conclude that recent employment rates are not a tipping point of a worrying trend. It seems that the deviation in recent LFS data from 2016 compared to 2015 is a statistical blimp, mainly due to the first quarter of 2015 for women. In other words, it is not a deviation from the general trend of a decreasing employment gap between men and women. Part-time employment, on the contrary, is still unequally divided between men and women. The part-time gap continues to exist even in the most recent period of economic upturn, as is shown by LFS data as well as Belgian administrative Dynam data on new entrants in firms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.053 | 0.016 |
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".