Políticas sociolaborales en las comunidades autónomas de Madrid y Canarias
Bibliographic record
Abstract
Aquest Treball de Fi de Carrera (TFC) analitza i avalua les polítiques sociolaborals de dos comunitats autònomes: Madrid i Canàries. Per realitzar-lo s’ ha investigat tant a nivell qualitatiu com quantitatiu, les politiques sociolaborals que es desenvolupant en les dos comunitats per mitja dels plans y programes d’ actuació, realitzats per les seves Consegeries de Govern. Per realitzar l'estudi quantitatiu de cada comunitat autònoma, s’ ha calculat i analitzat a nivell estadístic, les polítiques sociolaborals, per mitjà d'unes variables i ràtios determinats. Els programes que s' analitzen en aquest estudi són el Pla de Formació Professional que ve desenvolupant la Comunitat de Madrid durant els últims anys, i el Pla per a la Dona a Canàries.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".