Language Socialization and the (re)Production of Bilingual Subjectivities
Bibliographic record
Abstract
Language socialization is the human developmental process whereby a child or other novice (of any age) acquires communicative competence (Hymes 1972), enabling him or her to interact meaningfully with others and otherwise participate in the social life of a given community. Language socialization occurs primarily through interactions with older or otherwise more experienced persons (Garrett and Baquedano-López 2002; Ochs and Schieffelin 1984; Schieffelin and Ochs 1986a, 1986b), but also, in most cases, through interactions with peers (Dunn 1999; Farris 1991; Paugh 2005; Rampton 1995a). The child or novice’s development of communicative competence through such interactions is largely a matter of learning how to behave, both verbally and non-verbally, as a culturally intelligible subject. While mastering the formal features of the community’s language or languages so as to be able to produce grammatically and pragmatically well-formed utterances (Ochs and Schieffelin 1995), the child or novice must also learn how to use language in conjunction with various other semiotic resources as a means of actively co-constructing, negotiating and participating in a broad range of locally meaningful (though largely quite mundane) interactions and activities — a process that Schieffelin and Ochs (1996) characterize as ‘the microgenesis of competence’. 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".