Dislocating peripheries to the center: A tecnologia social reinventing repertoires and territories
Bibliographic record
Abstract
Although social innovation has gained increasing importance in recent decades due to its promise of promoting social change, critical scholars have identified a number of gray areas, notably numerous perspectives that fail to deeply question the conditions that maintain social inequalities and exclusion, and that are marked by the absence of the voices of those living in the so-called “peripheries.” Through the example of The Agency, we propose an alternative to hegemonic ways of understanding social innovation, one based on the Latin American concept of tecnologia social, which embodies a decolonial view. We make three contributions to the social innovation literature, thereby enriching the North-South debate. First, we illustrate a process of sociotechnical reconfiguration—an interplay of methodological tools, artifacts and discourses—which is central to the conception and implementation of a tecnologia social. Second, we show how a tecnologia social operates through a cumulative layering process of decolonizing the imaginary, challenging the colonial relationship between center/periphery and positioning deprived young people as actors who reinvent their repertoires and territories. Third, we introduce a debate linking the anthropophagic approach to epistemic justice, a valued theme in decolonial thinking. In doing so, this article contributes to the literature by proposing applicable and positive outcomes to critical and decolonizing thinking.
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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 | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.097 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.001 | 0.015 |
| 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".