Bibliographic record
Abstract
According to the results of the social balance sheets available in September 2008, employment rose by 2.3 p.c. in 2007. The increase concerned both full-time as well as part-time workers. The rise in part-time working is only partially explained by the hiring of part-time workers : this year again, medium-sized and large enterprises also saw numerous changes in working arrangements. Although women still account for the bulk of part-time workers, the increase in the part-time rate was greater for men than for women. The analysis shows that part-time working is spread unevenly in the various branches of activity. As in previous years, the article studies staff movements and the characteristics of workers joining and leaving companies filing full-format accounts. It also examines reasons for leaving and the external turnover of workers by comparing the results from the various groups of enterprises categorised according to their size or their branch of activity. The analysis also highlights the further advance in 2007 of temporary employment contracts – whose proportion is tending to become more uniform in the various categories of company size – and the increased use of agency work in companies filing full-format accounts. Recourse to these contracts as instruments for workforce adjustments varies considerably from one branch of activity to the other. The total wage bill rose by 5.1 p.c. between 2006 and 2007 in the reduced population of companies. Over the same period there was a 2.3 p.c. growth in the number of hours worked, so that hourly labour costs grew by 2.8 p.c. on average. The rise was more pronounced for full-time than for part-time workers. Major differences in levels are still discernible in terms of hourly costs, depending on the size and branch of activity of the companies. In terms of training, the results for 2006 still fall well short of the set targets : training costs accounted for 1.17 p.c. of the total wage bill whereas the target specified in the Generation Pact for this same year was 1.9 p.c. At the same time, the participation rate in training was only 35.2 p.c. whereas a target has been set of 50 p.c. by 2010. Growth was nevertheless recorded between 2006 and 2007 within a favourable economic context : the cost indicator for training grew by 4 p.c. and the participation rate by 0.2 p.c. If these increases were applied to the level observed in 2006, these same indicators should amount to 1.22 and 35.3 p.c. respectively for 2007.
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.064 |
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".