CELBAN: A 10-Year Retrospective Catherine Lewis & Blanche Kingdon
Bibliographic record
Abstract
This article provides a 10-year review by the test developers of the Canadian English Language Benchmark Assessment for Nurses (CELBAN™). From 2004 to 2014, the development, implementation, national administration, and operations of CELBAN and CELBAN-related products and services were the responsibility of the test developers and team at the Canadian English Language Assessment Services (CELAS) Centre at Red River College, Winnipeg, Manitoba. The CELAS Centre team experienced both challenges and opportunities during this 10-year period. As CELBAN expands, and in light of its current profile as a high stakes language assessment tool, a time for reflection and review is warranted. This retrospective review of CELBAN provides an overview of its history, administration, operations, and growth, as well as challenges experienced and lessons learned by the CELAS Centre team. Further research and development ideas are also posited by the CELBAN test developers. Cet article présente un examen décennal par les auteurs du CELBAN (Canadian English Language Benchmark Assessment for Nurses), l’évaluation de compétence linguistique pour infirmiers et infirmières. De 2004 à 2014, les auteurs du test et l’équipe au centre canadien des services d’évaluation de compétence linguistique en anglais (CELAS) situé au Red River College, à Winnipeg, au Manitoba, étaient responsables du développement, de la mise en œuvre, de l’administration à l’échelle nationale et des activités du CELBAN, ainsi que des produits et des services qui en découlent. Pendant ces dix ans, l’équipe du centre CELAS a affronté des défis et fait face à de nouvelles occasions. Compte tenu de la croissance du CELBAN et de son profil actuel comme outil d’évaluation linguistique à enjeux importants, une période de réflexion et de révision se justifie. Cet examen rétrospectif du CELBAN offre un aperçu de son histoire en évoquant son administration, ses activités, sa croissance, ainsi que les dé s affrontés et les leçons apprises par l’équipe du centre CELAS. Les auteurs du test proposent de nouvelles pistes de recherche et des idées de développement.
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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 | 0.027 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".