Applied Measurement and Evaluation
Bibliographic record
Abstract
Alberta. Since its inception 10 years ago, the mandate for CRAME has been clear: To enhance the quality of educational measurement, educational research, and program evaluation and to promote educational measurement and evaluation as an integral part of instruction. These outcomes are pursued within the context of the research conducted and courses taught by the faculty in CRAME. With ‘The Bulletin ’ we hope to promote the ideas in the Centre and to disseminate information about research activities and findings. ‘The Bulletin ’ will provide a way for CRAME’rs to interact with other professionals in Canada and abroad who have interests in educational measurement and evaluation. ‘The Bulletin ’ will be published on-line and distributed from the CRAME web site twice a year—in the Fall (September) and Winter (January) terms. Each issue will contain an up-date of the research conducted in CRAME, an overview of the activities in the Centre, and a feature article. In this issue our feature article is by Dr. Robert E. Stake, Professor of Education, University of Illinois, Urbana-Champaign. Dr. Stake was a visitor in CRAME and an EFF Distinguished Scholar at the University of Alberta in the Fall 1998 term. He spent a week at the University
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 | 0.086 | 0.143 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.036 | 0.017 |
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".