Identificação da Reputação de Áreas Urbanas Externas com Dados de Mídias Sociais
Bibliographic record
Abstract
Aprender a percepção das pessoas que emerge dasáreas urbanas tem sido um objetivo de pesquisa multidisciplinar, pois oferece um grande potencial para facilitar a difícil tarefa de compreender as características intrínsecas dasáreas urbanas, por exemplo, sua reputação. Para isso, comumente, são exploradas abordagens tradicionais de coleta de dados, como entrevistas. No entanto, tais métodos não escalam facilmente, dificultando a execução desse tipo de análise para um grande número de lugares. Para superar esse desafio, propomos um método alternativo que explora dados de redes sociais baseadas em localização (LBSNs). O nosso método inovador, chamado de REP-Map, trata da descoberta e mapeamento da reputação dasáreas urbanas externas, explorando aspectos semânticos e espaciais em mensagens compartilhadas em LBSNs. Estudando áreas externas de Chicago, mostramos, através de uma pesquisa com voluntários, que nosso método pode capturar a reputação que os usuários consideram em relação a essasmáreas urbanas externas.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".