"Les études européennes à l'Université de Montréal: une tradition "
Bibliographic record
Abstract
I hope that you had a good start to the New Year.ECSA-C started the year 2005 with new initiatives designed to strengthen its role as a source of information and platform for collaboration in the field of European Studies: Firstly, in October 2004 I had the opportunity to present ECSA-C activities at the ECSA World meeting in Brussels.Of particular interest to our partner organizations around the world was the EUCAnet project, a joint initiative of ECSA-C and the European Studies Program at UVic (Prof.Amy Verdun and myself) that is sponsored by the European Commission for one year.This project started in January 2005 and, next to a host of other outreach activities, will create an online directory for experts on Europe and the European Union in Canada.Other ECSA organizations expressed their strong interest in being updated about and, in the long term, possibly even involved in this project.The meeting showed that creating such online based directories (something that, to my knowledge, has so far only been tried by ECSA World, UACES and the Australian CESAA) is of critical concern to many ECSAs and that this is widely seen as a great resource to promote the network of scholars in the field, to give more visibility to European Studies in the wider community
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.036 | 0.003 |
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".