Plataformas de fazendas de cliques: condições de trabalho, materialidades e formas de organização
Bibliographic record
Abstract
Resumo Este artigo analisa as plataformas de fazendas de cliques como elemento de aprofundamento da plataformização do trabalho no Brasil. Com base em entrevistas com trabalhadores e observações em plataformas, grupos de WhatsApp, Facebook e vídeos no YouTube, a investigação destaca condições de trabalho, materialidades e formas de organização das pessoas que são pagas para curtir, comentar e seguir perfis em plataformas de mídias sociais. As fazendas de cliques agem como plataformas parasitas em relação às infraestruturas das mídias sociais. O perfil de quem trabalha para as plataformas é de mulher, com histórico de trabalho informal. Há mercados paralelos de venda e compras de contas fakes e bots, que fazem parte das táticas desses trabalhadores para sobreviver no contexto das fazendas de cliques.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".