Nourishing the Social Innovation Debate with the “Social Technology” South American Research Tradition
Bibliographic record
Abstract
Abstract Inspired by the South American research tradition known as “social technology,” this article proposes an operational framework to advance the understanding of mechanisms that help to promote social transformation. To illustrate its theorizing potential, we apply the framework to a nonprofit organization– Parole d’excluEs –that was created in Montreal (Canada) in 2006 and that has been promoting citizen mobilization and commitment to social change (parole-dexclues.ca). To that end, we offer a theoretical paper with an empirical illustration as a first step in a reflection on employing a global South theoretical lens–drawing on the concept of social technology–to make sense of a global North social innovation experience and to advance existing knowledge on the mechanisms of social transformation. The results contribute to social innovation research and practice, particularly at the interface between the management and nonprofit literatures.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.066 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.008 |
| 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".