Scientific Knowledge and Rural Policy: A Long‐distant Relationship
Bibliographic record
Abstract
Abstract This article examines the extent to which social science evidence is considered by community leaders in small towns and rural areas. It uses secondary analysis of 18 transcriptions from interviews in rural regions within two Canadian provinces to examine what types of support (if any) are used by respondents to justify their claims and assess the extent to which they depend on systematically collected and analysed evidence. The results indicate that the respondents seldom provided justification for their claims and when they did, scientific evidence was infrequently used. Instead, the respondents most often used examples from their personal experience or public meetings as support. Comparative analysis of the two rural region showed that the pattern of support was different in each – with respondents from B ritish C olumbia ( BC ) relying more on personal examples and those from N ewfoundland and L abrador ( NL ) relying more on public presentations or the internet. The results suggest that much work needs to be done to make social science evidence available and useful to those in small towns and rural places. According to those results, the most strategic way to begin is through existing networks, community groups, and local examples.
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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 | 0.042 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.014 | 0.052 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".