Bibliographic record
Abstract
Despite the fact that they are all unique, rural school districts/divisions (in Canada and elsewhere) face similar challenges when it comes to providing effective professional development (PD) for teachers. Issues related to funding, geography, staffing, and contextual differences impact the availability of PD opportunities for educators in rural contexts; however, rural school divisions possess many strengths from which solutions to these challenges might be fashioned. The question of how rural divisions might construct local teacher PD models that draw on local strengths, mitigate local challenges, and support teacher professional growth is critical to the provision of quality education for rural students. Through a single-case study design, this study examined the effectiveness of a rural initiative, the Numeracy Cohort, that was locally constructed to mitigate challenges and improve mathematics instruction and student numeracy outcomes in a school division in Manitoba, Canada. Findings from the study suggest that (a) the Numeracy Cohort model was effective in accommodating contextual differences and mitigating challenges related to funding, geography and staffing through several promising practices; (b) the PD provided to teachers was effective in supporting teacher professional growth in several ways; (c) attention to the multiple nested and dynamic contexts in which teachers worked was an important and effective element of the model; (d) fostering social interaction (among teachers and with more competent others) was important for teacher learning; and (e) finding ways to foster human engagement through mediating tools for learning (e.g., dialogue, reflection, and action research) was critical to the model’s success.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".