Bibliographic record
Abstract
Abstract\nRural educators face challenges in accessing timely, relevant, and appropriate professional learning opportunities in Yukon due to the complexities of living and working in geographically and professionally isolated communities (Hellsten, McIntyre, & Prytula, 2011). Complexities surrounding the political landscapes within Yukon also creates a unique set of challenges. Adding to the challenges, Yukon education is also in the process of implementing a new curriculum model that attempts to be more holistic and considerate of the needs of the people living and working within the Territory (Yukon Education, 2011). The current change processes and vision for Yukon education provides a unique opportunity to implement changes to the way rural educators access professional learning (Department of Education, 2018). This Organizational Improvement Plan (OIP) suggests the creation of a rural based professional learning model using Kotter’s Eight-Step Change Model (Cawsey, Deszca, & Ingols, 2016) within Deming’s Plan, Do, Study, Act change model (Donnelly & Kirk, 2015) to guide change while providing a framework for those involved in the change process to follow. Obstacles, challenges, and contextual realities are explored and considered within the OIP as well. The goal of the change plan is to provide a working framework for how rural Yukon educators access and acquire pedagogical efficacy in areas of interest and need through a collaborative process with other rural Yukon educators. This model is designed with rural Yukon educators in mind, but may be adapted to meet the needs of other rural jurisdictions.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".