Principles of Repopulation Initiatives for Community Resilience: Understandings Gleaned From the Proposed Scottish Government’s Islands Bond
Bibliographic record
Abstract
Depopulation is a concern for many islands and, in response, proposals and policies designed to reverse population loss have been developed in various national contexts. Adopting a community resilience lens, this paper aims to understand what lessons could be learnt from the proposed Scottish Government’s Islands Bond and other initiatives designed to attract new island residents to inform any future repopulation policies targeting sub-national islands. We highlight some of the underlying ambitions of repopulation interventions and the ways in which they contribute to community resilience. We conclude that to be successful as a means of supporting community resilience, repopulation initiatives need to incorporate six underlying Principles of Repopulation Initiatives for Community Resilience. These sit under three key themes: principles of retaining and supporting population and in-migration, place-based principles of initiative design, and principles for measuring success.
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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 | 0.013 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.026 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".