Bibliographic record
Abstract
Abstract The sociocultural complex of the northwest Amazon is remarkable for its system of linguistic exogamy in which individuals marry outside their language groups. This article illustrates how linguistic exogamy crucially relies upon the alignment of descent and post-marital residence. Native ideologies apprehend languages as the inalienable possessions of patrilineally reckoned descent groups. At the same time, post-marital residence is traditionally patrilocal. This alignment between descent and post-marital residence means that the language which children are normatively expected to produce – the language of their patrilineal descent group – is also the language most widely spoken in the local community, easing acquisition of the target language. Indigenous migration to Catholic mission centers in the twentieth century and ongoing migration to urban areas along the Rio Negro in Brazil are reconfiguring the relationship between multilingualism and marriage. With out-migration from patrilineally-based villages, descent and post-marital residence are no longer aligned. Multilingualism is being rapidly eroded, with language shift from minority Eastern Tukanoan languages to Tukano being widespread. Continued practice of descent group exogamy even under such conditions of widespread language shift reflects how the semiotic relationship between language and descent group membership is conceptualized within the system of linguistic exogamy.
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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 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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".