Bibliographic record
Abstract
Agência de Redes is a Brazilian project that aims to promote citizenship awareness and social inclusion of young people living in favelas in the city of Rio de Janeiro. By developing and deploying its methodological process of destruction of old and construction of new interpretative frames amongst socially excluded young people, Agência de Redes is an example of an emerging South American concept of social technology (ST) that is still little documented in the North American and European literature. The methodology used by Agência de Redes presents to young favela residents a new vocabulary - such as inventories, maps, cabinets of curiosities, abecedarian and bestiary - that is used to help them develop ideas for intervention in their territories. Throughout the methodological process, young favela residents are exposed to situations where they are forced to revise their taken-for-granted assumptions, to fix new meanings and engage in active social change. By using critical discourse analysis (CDA), we may see how the political discourse held by Agência de Redes mobilizes other discourses and concepts to create new social practices that, in turn, may conversely reinforce some discourses that were initially aimed to be undermined. In this article, I therefore attempt to analyse an empirical illustration with the ultimate purpose of contributing to the literature of ST by presenting how CDA may be useful to better understand this emergent phenomenon of ST.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".