Andean Spanish and the Spanish of Lima: Linguistic Variation and Change in a Contact Situation
Bibliographic record
Abstract
One of the frequently mentioned results of globalization has been its detrimental effects on the maintenance of minority languages. It has been estimated that of the roughly 6,000 languages spoken across the globe in 2000, between 50 per cent and 90 per cent will not survive the twenty-first century. For the Quechua-speaking masses in Peru, who lived in relative isolation in the Andean region following the Spanish invasion in the sixteenth century, the twentieth century brought increased opportunities for contact with Spanish speakers as a result of the modernization of the economy, the development of communication networks and the initiation of massive migration from the Andean region to the coast. These changes have brought about a rather rapid language shift from Quechua to Spanish, as is apparent in census data. In 1940 over half the population of Peru spoke an indigenous language. However, by the 1980s only one-quarter of the population claimed some proficiency in one of these languages. According to census data, approximately 60 per cent of those who speak an indigenous language in Peru also speak Spanish (Pozzi-Escot, 1990). Thus, there has been fairly rapid language shift in Peru over the past 65 years. Mufwene (2004: 207) has described language shift among Native Americans in ecological terms, as ‘an adaptive response to changing socioeconomic conditions, under which their heritage languages have been undervalued and marginalized’. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".