Bibliographic record
Abstract
The program for this conference on empirical issues in Canadian Education offers a picture of the national and provincial policy and program agenda. Queens University’s John Deutsch Institute, Statistics Canada, WRNET and Arthur Sweetman and Patrice de Broucker are to be congratulated for giving us an opportunity to discuss these critical issues. This conference is very important as it creates a forum for empirical story telling, an important event for class rooms, schools, at the school trustees tables, ministries of education and in the federal government’s support of human resource development, innovation and the new economy, filling data gaps and scholarly research. In this paper I am suggesting that we need to better understand where teaching and learning performance management data fits in and how are education leaders using or not using data as we work to strengthen empirical research in Education. I am also putting forward the argument that we need to support the creation of research databases that will make data about the K-12 educational system in British Columbia and elsewhere available, in a user-friendly way, to school districts and schools, researchers, policymakers and other qualified individuals and organizations in a wide range of social science specialty areas, subject to privacy and confidentiality guidelines. This paper is not a criticism of our current assessments initiatives. It attempts to take a first look at an under examined corner of our practice – performance measurement and the organization of teaching and learning in our schools. At Edudata Canada we are working with academic, school district and policy partners to make developmentally focused analyses feasible, that is, making it possible to link changes over time in student-level outcomes (achievement, attitudes, course taking, etc.) to systemic initiatives. In several jurisdictions outside British Columbia, student-level outcome data is either not available, 3 or it cannot be linked to the appropriate variables at the student level. Reports and analyses issued by ministries of education as well as by national and international organizations such as
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.204 | 0.026 |
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".