Bibliographic record
Abstract
This article, part of a collective research study on the role of expertise in implementing employment policy programmes, focuses on the activities—specifically, making judgments and interacting with others—of public-sector personnel working in the framework of one of France’s many experimental employment programmes, the Occupational Transition Contract. This unique, innovative project is in many respects in the spotlight of French current events. The special expertise of public agents working to assist redundant employees may be observed in how they successively or simultaneously use empathy, understanding and an ability to objectify throughout their interactions with persons “enrolled” in the programme. After detailing the programme’s potential opportunities, we apply Christian Bessy and Francis Chateauraynaud’s sociology of perception to study the dominant type of expertise used in it. We then bring in components of disposition sociology to bring to light the segmentation distinguishing actors from one other, the point being to explain regularities in types of expertise and how those may oscillate depending on the conditions in which the expertise is proffered and the varied, polymorphous profiles and experience of members of the small groups in charge of establishing and implementing the Occupational Transition Contract in particular areas of France. Attention to these activities and to interaction between programme “referents” (counsellors) and members brings to light how labour norms and accords have been profoundly transformed in the shift from the “lifelong job” notion to that of “sustainable employability.”
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.959 | 0.970 |
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; the direct Gemma label and the distilled Codex classifier 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".