Da integração das Américas a um cemitério de pipas: a construção de um projeto de inclusão digital na Favela da Maré
Bibliographic record
Abstract
EnglishThis paper aims to build the history of a project that connected computer centers located in Mare, a 'favela' (slum) in Rio de Janeiro, to Rede Rio, the computer network formed by the leading research institutions of the state of Rio de Janeiro. That connection was established through wireless links, in a move that set up a network of actors as the Federal University of Rio de Janeiro, the Canadian government agency Institute for Connectivity in the Americas (ICA), a couple of non-governmental organizations (NGOs), routers, antennas, the Mare's inhabitants, among others. Referenced on Actor-Network Theory, the goal is to map the translations that made the project a stable entity, as well as changes in these translations which destabilized it some years later. portuguesEste artigo tem como objetivo construir a historia de um projeto que conectou centros de informatica da Mare, favela da cidade do Rio de Janeiro, a Rede Rio, rede de computadores formada pelas principais instituicoes de pesquisa do estado do Rio de Janeiro. Os laboratorios foram conectados atraves de links sem fio, em um movimento que configurou uma rede de atores composta pela Universidade Federal do Rio de Janeiro, pela instituicao governamental canadense Instituto Para a Conectividade nas Americas (ICA), por organizacoes naogovernamentais (ONG), roteadores, antenas, moradores da Mare, dentre outros. Fazendo uso da Teoria Ator-Rede, procura-se mapear as traducoes que fizeram do projeto uma entidade inicialmente estabilizada, bem como as mudancas nestas traducoes que desestabilizaram-no alguns poucos anos depois.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".