Bibliographic record
Abstract
Este trabalho investiga de que maneira a história tem sido colocada dentro da literatura, no caso específico do texto literário pós-moderno. As complexidades de troca entre história, ficção (estória) e narrativas de vida em Green grass, running water são analisadas a partir de uma série de formulações. O “Massacre de Wounded Knee”, Dakota do Sul, de 1890, e seu desdobramento, a “Batalha em Wounded Knee”, que ocorreu na década de 1970, são os episódios históricos analisados a partir da perspectiva metaficcional, pós-modernista, apreendida no romance acima citado, do escritor indígena canadense Thomas King.Abstract: This paper investigates how history has been placed within literature, in the specific case of the post-modern literary text. The complexities of the exchange between history, fiction (story) and life narratives are analysed as they appear in Green grass, running water from a series of formulations. The “Massacre at Wounded Knee,” South Dakota, of 1890, and its redoubling, the “Battle at Wounded Knee”, which took place in the 1970s, are the historical episodes analysed from a metafictional, post-modern perspective, as apprehended in the above mentioned novel by the Canadian native writer Thomas King.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".