INTRODUZINDO A INVESTIGAÇÃO NARRATIVA NOS CONTEXTOS DE NOSSAS VIDAS: UMA CONVERSA SOBRE NOSSO TRABALHO COMO INVESTIGADORES NARRATIVOS
Bibliographic record
Abstract
O texto relata nossas experiências como investigadores narrativos para destacar aspectos da investigação narrativa que, frequentemente, criam dificuldades para novos pesquisadores a esta forma relacional de pesquisa. A investigação narrativa é o estudo da experiência entendida narrativamente. Dessa forma, chamamos a atenção para a investigação narrativa como um fenômeno sob estudo e metodologia para o estudo. Isso por si só, frequentemente, cria confusão para aqueles que são novos na investigação narrativa. Destacamos três tensões particulares que, frequentemente, causam dificuldades para os pesquisadores. Engajando no início da narrativa autobiográfica, mudando de textos de campo para textos de pesquisa e conduzindo investigações narrativas tornadas propositais para justificativas pessoais, teóricas/práticas e sociais foram as três tensões que selecionamos. Baseando em investigações narrativas contínuas e recentemente concluídas, Dilma, Shaun e Jean tornaram explícitas as maneiras como mudaram de textos de campo provisórios para textos finais de pesquisa.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.018 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| 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".