Bibliographic record
Abstract
In V ietnamese, address (second‐person reference) is typically accomplished by the use of a kin term regardless of whether the talk's recipient is a genealogical relative or not. All Vietnamese kin terms encode a specification of either relative age or relative generation of participants, and there are no reciprocal terms akin to E nglish ‘brother’ or ‘sister’; rather, a speaker must select between terms such as ‘older brother’ ( anh ) or ‘younger sibling’ ( em ). Since generation is normatively associated with a difference in age, the result is a ubiquitous indexing of age and status hierarchies in all acts of address. This results in a problem for peers. How, in such a system, should they address one another (and also self‐refer)? In this article, we describe the various practices that speakers use to subvert the system and thus avoid indexing differences of age or station. Specifically, we describe four practices: (1) the use of true pronouns in address and self‐reference; (2) the use of proper names in address and self‐reference; (3) the use of kin terms in address and pronouns in self‐reference; and (4) the ironic use of kin terms in address. We conclude that the Vietnamese system well illustrates what is likely a universal tension between hierarchy and equality in acts of address and self‐reference, by showing how speakers deconstruct the vector of age and indicate that they consider one another peers. We further suggest that although the literature in this area has focused on the ways in which languages convey differences of status and rank, social order is built as much upon relations of parity and sameness – on identification of the other as neither higher nor lower than me – as it is upon relations of hierarchy.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".