Special issue on advocacy for change in mathematics and science education
Bibliographic record
Abstract
[Extract] When the Natural Sciences and Engineering Council (NSERC) established the centres for Research in Youth, Science Teaching and Learning (CRYSTAL) in 2004 it was responding to concerns about improving the quality of the science and mathematics experience provided for Canadian students. The focus of the University of Manitoba CRYSTAL was to understand the multisystem influences on the learning experience provided for students and, in turn, respond with actions that would improve the quality of these experiences. In the October 2007 issue of the Canadian Journal of Mathematics, Science and Technology Education (CJMSTE) we introduced readers to several of the contexts in which our CRYSTAL research was situated and the research being initiated in these contexts. Although the contexts of our 17 research projects were diverse, the common theme among them, as illustrated by some of the projects in that issue, was that any improvements in experience provided for students needed to begin by listening to the current experiences of participants and, in turn, responding to their voiced concerns.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.016 | 0.009 |
| Insufficient payload (model declined to judge) | 0.089 | 0.023 |
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".