Building reciprocity: From safety‐net to social transformation programmes
Bibliographic record
Abstract
Abstract Topics of societal concern such as mental health and poverty reduction increasingly require action programmes that operate within broad psychosocial and social justice perspectives. Models of practice centred in individual needs, although important, are not powerful enough to bring about social change when they operate in isolation. In this article, we present the findings resulting from the observation of programmes engaged in collaborating with socio‐economically disadvantaged individuals, families, and communities. The programmes selected for study were nationally or internationally recognized for the quality and innovation of their methodologies or for having been subjected to scientific attention; some met both criteria. Altogether, 15 programmes were visited, in North and South America and Europe. Through a grounded theory methodology, the processes of data collection and analysis led to the development of a theoretical framework that identifies a continuum of programmes aimed at supporting the development of individuals, families, and communities and that has at its core the central process of building reciprocity. This article presents and describes the continuum of programmes and how each type relates to the process of building reciprocity and establishes links with other relevant and significant concepts in the framework. Finally, implications for further research are explored.
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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 | 0.018 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".