Building teaching capacities in higher education : a comprehensive international model
Bibliographic record
Abstract
1) Preface-Alenoush Saroyan and Mariane Frenay 2) Prologue-James Groccia SECTION 1: FIVE CASE STUDIES AND A COMPARATIVE ANALYSIS 3) Faculty Development in Canadian Universities-Lynn K. Taylor and Denis Bedard 4) Faculty Development in Switzerland: A Study of French Speaking Universities-Nicole Rege-Colet 5) Danish Faculty Development Strategies-Anette Kolmos 6) Faculty Development in Belgian Universities-Mieke Clement and Mariane Frenay 7) A Glance at the French Experience in Faculty Development-Jean-Jacques Paul and Noel Adangnikou 8) Faculty Development Across Europe and Canada: Comparisons of Five Case Studies-Mariane Frenay and Alenoush Saroyan SECTION 2: DEVELOPMENT AND VALIDATION OF A CONCEPTUAL FRAMEWORK 9) Making the Shift from Faculty Development to Educational Development: A Conceptual Framework Grounded in Practice-Lynn K. Taylor and Nicole Rege-Colet 10) Validation of a Conceptual Framework: The Meaning and Scope of Faculty Development-Denis Bedard, Mieke Clement, and Lynn K. Taylor 11) Epilogue-Kirsten Hofgaard Lycke Index.
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 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".