ESRC Staying Rural Policy/Evidence Briefing Series (1-4)
Bibliographic record
Abstract
STAYin(g)Rural is an international project jointly funded by the Economic and Social Research Council (ESRC), Netherlands Organisation for Scientific Research (NOW), and German Research Foundation (DFG). The project involves collaboration between three Universities: Queen’s University Belfast (NI), University of Groningen (the Netherlands), and the Johann Heinrich von Thünen Institute (Germany). The aim of the project is to understand how and why people stay in rural areas at different life transitions, and the contributions they make to rural communities and rural quality of life. The findings will help inform rural policy decisions concerning funding, service provision and living conditions. This series of four research/policy briefings, each four pages long, focuses on the Northern Ireland case study, Clogher Valley, situated in Co. Tyrone. The briefings explore: 1. Why do people stay in rural areas?, 2. How is rural staying best enabled?, 3. What are the obstacles to rural staying?, 4. Rural staying across the life course: preferences, barriers and opportunities. It is an end of ESRC project series of research briefings aimed at academics, policy makers, and the general public. They also formed the basis of posters, which were exhibited locally. The briefings are not published but this is an important project resource. It is most akin to an end of project report. There are 4 in the series , numbering 16 pages.
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 | 0.098 | 0.192 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.015 | 0.016 |
| Insufficient payload (model declined to judge) | 0.162 | 0.040 |
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".