Bibliographic record
Abstract
Rural school divisions in Canada, if they can be spoken of as a collection, consist of extremely diverse groups of people, living in varied geographical settings, with unique community strengths and challenges. The provision of professional development (PD) for rural teachers is one area rural school divisions face challenges due to the contexts in which they operate. Issues relating to funding, geography, staffing, and local contextual differences impact the ability of rural divisions to provide meaningful PD for their teachers. As a result, locally-constructed solutions must be conceptualized to leverage the unique strengths inherent in rural contexts in order to mitigate some of the challenges faced by small rural divisions with regards to the provision of effective PD for teachers. The qualitative, single case study outlined in this session inquires into the effectiveness of one locally-developed teacher PD model implemented in a small rural school division in Manitoba to improve student numeracy skills. The model, which was designed specifically to mitigate challenges faced within the local context, provides an opportunity for inquiring into the both the nature of the challenges faced in rural contexts, and the ways local solutions can be constructed.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".