Bibliographic record
Abstract
(EKETEP), a preservice preparation program designed for rural school districts and,citizens. Established as a collabo-rative venture ofthe University, a community college, and the provincial government, EKETEP is located in southeastern British Columbia, Canada, in the East Kootenay town of Cranbrook. In 1992, the first EKETEP cohort completed the preparation and certification process. These 22 teachers then participated in a 5-year study focusing on key aspects of their experience: career activities, career goals and preferences, perspectives on teaching, employment strategies, and reflection on preservice experience. This article reviews the findings of the study and their implications for teacher preparation and recruitment. The intent was to capture data regarding the early years of these teachers ' professional experience to provide a reference for program planners and teacher employers. Canadian provinces and the federal government peri-odically undertake systematic reviews of key social ser-vices or major issues. The most recent British Columbia Royal Commission on Education (1988) provided a com-prehensive review of the circumstances and conditions of schooling in the province. It identified some of the unique dimensions of rural life and education, and "the problem
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".