Bibliographic record
Abstract
Ministers of Education of Canada (CMEC) as part of the general framework of the 2001 conference on Training teachers and educators: priority topics and issues/related research subjects. More specifically, our report deals in a Canada-wide perspective with the issue of societal conditions that have prompted government agencies in North America, and especially in Canada and Quebec, to develop and offer preschool education services to ever-younger segments of the population. We first deal briefly with the background history of preschool education to the extent that this history is situated first and foremost in a twofold perspective of the government's response to women's need for access to the job market and the need for compensatory intervention in materializing equality of opportunities for educational success among low socio-economic communities. Subsequently, we deal briefly with the status of teaching-specific training at preschool level, for classes of four- and five-year-olds (maternelle in French, pre-K and kindergarten in English-language systems) as part of current teacher training programs in most provinces in Canada. We particularly highlight the variability of the degree to which the mission of kindergarten teaching is taken into consideration, depending on teacher education faculty and
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.480 | 0.254 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".