Bibliographic record
Abstract
In a range of eastern and southern African language communities, stretching from Ethiopia to the Cape, married women are enjoined to avoid the names of members of their husband's family as well as (near-)homophones of those names, and to replace tabooed vocabulary with substitute words. Although in-law name avoidance is a global phenomenon, the daughter-in-law speech registers thus constituted are unusual in their linguistic elaboration: they involve avoidance not only of names and true homophones of names but also an array of words whose only relation to tabooed names is phonological similarity. We provide an overview of the distribution and convergent social and linguistic characteristics of these registers and then examine one register more closely, namely, that of Datooga of Tanzania. To tease apart the layers of causality that converge upon this particular sociolinguistic pattern, we consider archaeological, ethnological, sociolinguistic and genetic lines of evidence. We propose that any partial diffusion of in-law avoidance practices has been complemented by a complex of sociocultural factors motivating the emergence of this pattern at different times and places across the African continent. These factors include pastoralism, patrilineal descent ideologies and norms of patrilocal postmarital residence paired with cattle-based bridewealth exchange.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".