Escritor / Qillqaq: The Representation of Peruvian Literature in the Spanish and Quechua Wikipedias
Bibliographic record
Abstract
This paper explores the construction of Peruvian literature in two different language editions of Wikipedia: Spanish and Quechua. Comparing both versions I analyze the way they approach the representation and writing of a national literature in relation to two factors: region (writers from the 25 Peruvian political regions) and language (words used to provide information about writers). I argue that the exclusion of cultural contributions of Amazonian regions in both Wikipedias not only has a high correlation with the regional population and Gross Domestic Product, but also reproduces a traditional notion of Peruvian literature. However, because of its accessibility and collaborative nature, the Quechua Wikipedia innovates by enabling the user community to propose an alternative representation of national literature. The research also demonstrates the urgency of engaging and fostering the participation of indigenous communities in the creation and editing of Wikipedia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".