Bibliographic record
Abstract
The newest Canadian Elementary Health and Physical Education (2019) provincial curricula promotes inquiry as a pedagogical mode. AR complements this inquiry mode of instruction with its grounding in experience and practice which infuses educational roles. AR as practice-based inquiry helps new educators identify and reveal resolutions; however, first a need to want to improve needs to be identified, before next steps are taken. AR has the potential to open doors of perception, trigger new insights, and cultivate teacher development within teacher training and beyond while in-service. Admittedly, teachers change, no matter how incrementally, which permeates professional development, as witnessed in over 100 years of action research drawn upon herein. Extant AR literature is grounded in the educational development of participants as they teach. Development in AR is not actually a problem needing investigation; instead it remains a possibility that needs recursive attention to ensure it exists within the training of educators globally. Herein AR is illustrated via narrative accounts that reflect experiences while teacher training in an Ontario Faculty of Education programme.
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 | 0.072 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.043 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".