Growing Innovation in Rural Sites of Learning
Bibliographic record
Abstract
The implementation of a revised curriculum in British Columbia, Canada’s rural schools and school districts is providing rich opportunities to study and document processes that support and prompt system change, as well to generate knowledge that can be shared across the province and more widely. This project aimed to study closely the practices and structures within BC’s Growing Innovation in Rural Sites of Learning professional learning network (PLN), to examine how this partnership between a university, the Ministry of Education, and the BC Rural Education Advisory is spurring innovation through collaborative, inquiry-based professional learning. This study examined how a PLN can generate and mobilize knowledge related to innovative and effective practice, particularly across rural or remote communities, and the role of PLNs in provoking and sustaining educational innovation. Key findings revealed that innovation occurs when educators find openings and gaps that create space and necessity for change, and that collaboration and reflection are key factors in sustaining and spreading innovation. Key drivers of this change included the new curriculum in BC as well as student learning needs and the challenges of the various rural contexts. Key factors in sustainability included administrative and district support as well as the ability to share their learning, including within the network.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".