Bibliographic record
Abstract
The theory-practice blend is an issue which must be addressed by Faculties and Schools of Education in the conceptualization and implementation of their "initial" teacher preparation programs. In this paper I will elaborate on some of the problems encountered in attempting to obtain the "right blend" in the theory-practice issue followed by some suggestions of what might be done to improve the situation. I will end by sharing with you some "hunches" I hold regarding the issues which surround questions related to theory and practice. My comments and observations are based on experiences gleaned in the implementation of the Extended Practicum and Core Undergraduate Programs at the University of Calgary.
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 | 0.306 | 0.281 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.012 | 0.092 |
| Scholarly communication | 0.045 | 0.075 |
| Open science | 0.006 | 0.031 |
| Research integrity | 0.017 | 0.035 |
| Insufficient payload (model declined to judge) | 0.007 | 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".