Advancing Social Care Through Evidence: A Position Statement from the International Network for Social Intervention Assessment (INSIA)
Bibliographic record
Abstract
The International Network for Social Intervention Assessment (INSIA) is a partnership of organizations that produce, support, and use evidence synthesis in social care. It emerges as a pivotal entity that supports evidence-informed social care decision-making. The paper argues for the importance of the production and use of trustworthy evidence synthesis in social care to inform and guide policy and practice. We articulate INSIA's position in the global field of social intervention assessment, list the vision and aims of INSIA as a network for promoting and enabling the production and use of evidence synthesis in social care, and issue a call to action, inviting other organizations to join this work.
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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 | 0.533 | 0.441 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.010 | 0.037 |
| Scholarly communication | 0.040 | 0.038 |
| Open science | 0.015 | 0.038 |
| Research integrity | 0.069 | 0.104 |
| Insufficient payload (model declined to judge) | 0.003 | 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".