Três faces do ChatGPT
Bibliographic record
Abstract
Com o objetivo de contribuir ao campo de estudos dos imaginários dos objetos técnicos, o presente artigo buscou mapear e problematizar as imagens condensadas no ChatGPT (Generative Pre-trained Transformer), modelo de linguagem da start-up OpenAI. Para isso, ancorado na perspectiva teórica da antropologia do imaginário, o estudo das imagens veiculadas pelo ChatGPT envolveu as seguintes estratégias metodológicas: o exame do contexto de produção do artefato; a análise do texto de lançamento do sistema e dos princípios que regem seu funcionamento; a realização de diálogos experimentais com o ChatGPT (denominados de DiEx); além de levantamento em literatura especializada. A partir disto, esta pesquisa exploratória identificou e caracterizou três faces que emergem do ChatGPT: científica, alucinatória e modulável pelo usuário. Por fim, propôs-se uma reflexão sobre as implicações das imagens consteladas pelo ChatGPT e a importância da diversificação dos imaginários na era da criatividade aumentada.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".