Multidimensionality of assessment in the Common European Framework of Reference for languages (CEFR)
Bibliographic record
Abstract
This article intends to discuss complexity of assessment by presenting its several layers and dimensions as they are conceptualized in the Common European Framework of Reference for languages (CEFR) and to show how the CEFR advocates an inclusive vision of assessment able to integrate several perspectives. After presenting the CEFR perspective of the nature and role of assessment, the article investigates some challenges practitioners are facing and their needs as to the assessment process. It also aims at casting light on the actual and potential impact of the CEFR on assessment cultures in different contexts. The data presented in this article, collected within the ECEP (Encouraging the Culture of Evaluation among Professionals) project of the Council of Europe and within its extension in the Canadian context, will help to understand why the CEFR can be seen as a relevant awareness-raising tool in the domain of assessment and beyond.
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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 | 0.056 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.004 | 0.025 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".