Bibliographic record
Abstract
Abstract Language socialization is a theoretical and methodological paradigm that originated in the discipline of anthropology, with the goal of addressing the relationship between culture and language learning. Scholars of language socialization use methods from ethnography, field linguistics, and sociolinguistics to document and analyze patterns of language use in communities. In the 1980s, anthropologists developed the paradigm of language socialization in response to a lack of attention to the diversity of languages and cultures represented within the study of first-language acquisition. To center inquiries into language learning around cultural and linguistic diversity, language socialization attends to everyday practices of language and communication as well as to the enduring language attitudes and cultural belief systems that co-constitute language structures. Language socialization emphasizes that humans build social identities, cultural practices, and senses of belonging as they learn and use languages. While language socialization originated in the study of young children’s first-language acquisition, it has since expanded to examine broader contexts of language learning. Guided by the understanding that the structures of real-time interactions and social institutions mutually create one another, language socialization scholars have examined how our social roles in families, schools, and professions shape our language use across the lifespan. Since the 1990s, language socialization research has taken particular interest in the relationship between language and power, drawing from theories of language ideologies—or taken-for-granted beliefs about languages and their speakers—to address topics related to multilingualism, including code-mixing, second-language learning, heritage-language learning, and language shift and revitalization. In 2023, key debates in the field focus on defining learners’ identities and highlighting communicative diversity beyond spoken languages.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.002 |
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".