An Individual Mixed‐Evaluation Method for Career Intervention
Why this work is in the frame
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Bibliographic record
Abstract
Economic issues linked to career counseling are a cause for concern to policy makers in developed countries because they expect career practitioners to provide evidence of the efficiency of career counseling interventions. The aim of this study was to test an individual evaluation method mixing time series (outcomes) and life narrative (processes). The method used 5 items related to 1 client's career decision self‐efficacy and studied the evolution of those items throughout the intervention of 1 career counselor (43 days). Changepoint analysis helped in identifying the changes that have to be taken into account for time series and which are contextualized in the client's verbatim analysis. This mixed method highlighted that the career counselor's intervention increased the client's career decision self‐efficacy. Practitioners could use the methodology proposed in this article to evaluate their interventions. They could also report their practice to clients, employers, and decision makers.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it