Bibliographic record
Abstract
One of the key deliverables for the Rural Policy Learning Commons is a graduate certificate in rural policy. As a foundation for this certificate, a research project was undertaken in 2015 and 2016 with the goal to identify subject areas that are essential for attaining competency in rural policy. This workshop has two key components: (1) a report on the project’s research findings, and (2) a group discussion among the workshop participants guided by a series of open-ended questions pertinent to identifying competencies for rural policy practitioners.1. The research project involved a comparative analysis of 22 Canadian Masters of Public Policy (MPP) and Master of Public Administration (MPA) programs and the learning outcomes presented at the 2015 International Comparative Rural Policy Studies (ICRPS) summer institute. The scan of 22 MPP/MPA programs revealed a marked absence of policy training focused on rural issues and yet an important congruence in the learning offered in the MPP/MPA programs and the summer institute. The analysis showed that training in analytical tools and socio-political contexts is foundational for policy design and implementation. However, acquiring competency in rural policy also requires coursework centred on rural policy sectors. 2. The capacity building focus of the workshop is a self-reflexive exercise, asking participants to discuss and report on a set of questions:a. What knowledge sets, skills, and attitudes are expected of rural policy practitioners?b. What gaps in knowledge, skills, and attitudes appear to be evident among rural policy practitioners?c. What organization and/or institutions offer training in these areas?d. Are rural and public policy conceptually, politically, or practicably distinct?
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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".