Defining and selecting key competencies.
Bibliographic record
Abstract
Preface, Heinz Gilomen, Swiss Federal Statistical Office Introduction: An overview, Dominique Simone Rychen, Swiss Federal Statistical Office Competencies for life: A theoretical and empirical challenge, Laura H. Salganik, Education Statistics Services Institute, American Institutes for Research, USA Concepts of competence: A conceptual clarification, Franz Weinert, Max Planck Institute, Germany Concepts of competence: A historical perspective, John C. Carson, University of Michigan, USA Key competencies: A philosophical perspective, Monique Canto-Sperber, Centre National de la Recherche Scientifique, France Jean-Pierre Dupuy, Ecole Polytechnique, Centre de Recherche en Epistemologie Appliquee, France Key competencies: A psychological perspective, Helen Haste, University of Bath, UK Key competencies: A sociological perspective, Philippe Perrenoud, University of Geneva, Switzerland Key competencies: An economic perspective, Frank Levy, Massachusetts Institute of Technology, USA Richard J. Murnane, Harvard University, USA Key competencies: An anthropological perspective, Jack Goody, St. John's College, University of Cambridge, UK Common ground: Functioning in groups and managing emotions, Cecilia Ridgeway, Stanford University, USA Common ground: Competencies as working epistemologies, Robert Kegan, Harvard University, USA Key competencies: Viewpoints from policy and practice, Jacques Delors and Alexandra Draxler, Task Force on Education for the Twenty-first Century, UNESCO Jean-Patrick Farrugia, Le Mouvement des Entreprises de France (MEDEF), France Bob Harris, Education International George Psacharopoulos, University of Athens, Greece [formerly with the World Bank] Laurell Ritchie, Canadian Auto Workers, Canada Leonardo Vanella, Centro de Estudios e Investigacion del Desarrolo Infanto Juvenil, Argentina Towards a theoretical and conceptual framework, Dominique Simone Rychen, Swiss Federal Statistical Office Laura H. Salganik, Education Statistics Services Institute, American Institutes for Research
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.054 | 0.025 |
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".