O MOVIMENTO MAKER: ENFOQUE NOS FABLABS BRASILEIROS
Bibliographic record
Abstract
O movimento maker, cada vez mais intenso em se tratando de praticas para a inovacao, vem ganhando espaco nas diversas regioes do mundo. No Brasil, muito se fala e muitas acoes sao observadas para se ter, principalmente junto a empreendedores, espacos passiveis de testar e desenvolver projetos. Especificamente tratando do termo FabLab, criado pelo Massachusetts Instituteof Technology (MIT), ha atualmente presenca em mais de 60 paises e no Brasil as iniciativas chegam a 17. Em comparacao com os indices mundiais, o Brasil fica em oitava colocacao no numero de FabLabs vinculados ao MIT, estando atras de paises como Estados Unidos, Franca, Italia, Alemanha, Holanda, Reino Unido e Espanha. Entretanto, apresenta um numero maior de FabLabs de paises desenvolvidos como a Belgica, Japao, Canada, Suica e Portugal. Os Laboratorios de Fabricacao brasileiros sao em sua maioria profissionais (nove) e universitarios (oito). Para a realizacao das atividades possuem maquinas como impressao 3D, fresagem CNC, circuito de producao, corte a laser / gravura, fresagem de precisao e vinil plotter. Entretanto, considerando a democratizacao de acesso aos FabLabs, com a abertura de pelo menos um dia para a comunidade geral, apenas sete indicam o open day. Palavras-chaves: FabLabs; Espaco Maker; Inovacao; Tecnologia; Criatividade.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".