Ethnocultural Code of the Brazilian Novel (1902–1922s): On Revealing and Discription
Bibliographic record
Abstract
The focal points of the study are the issues concerning Brazilian ethnoculture scrutiny and the means of its verbalisation in fictional texts. The study is devoted to the problems of ethnocultural code identification and description. It is carried out within multidisciplinary approach and with special software application in order to verify the preliminary outcomes. The criteria for cultural and ethnocultural codes differentiation are suggested, the author's methodology of philological research into ethnocultural codes and complex verification of the results obtained is tested. The data was obtained from the corpora of precedent Brazilian novels, published in the first quarter of the 20 th century (1902–1922s). This period of Brazilian literature is called Pre-Modernism, it has insufficiently been studied by Russian researchers as of yet. The results of social-and-humanitarian expertise and linguistic analysis as well as quantitative characteristics have enabled the author to reveal and describe ethnocultural code, which is contained in Brazilian fictional texts of the first quarter of the 20 th century. This code is shown as a structure, which is comprised of the ethnocultural codes of sertões, Bahia, race, space, and religion. The thematic groups of lexical units that verbalize each of the above-mentioned codes are characterized, the peculiarities of their functioning in the texts are identified.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".