Academic English Socialization Through Individual Networks of Practice
Bibliographic record
Abstract
This article introduces the notion of individual network of practice (INoP) as a viable construct for analyzing academic (discourse) socialization in second language (L2) contexts. The authors provide an overview of social practice theories that have informed the development of INoP—community of practice (CoP; Lave & Wenger, 1991; Wenger, 1998) and social network theory (Milroy, 1987)—and review relevant literature on academic discourse socialization and more general L2 learning studies that have used either CoP or social network as theoretical frameworks. Next, they illustrate how INoP was applied in a study that examined the academic English socialization of Mexican students at a Canadian university. Findings from the INoP analysis of three participants provide evidence of its rich potential for examining academic (discourse) socialization processes in other contexts and possibly using complementary forms of data analysis involving the analysis of interactional data. The authors suggest future applications of INoP in TESOL to help refine and validate this construct. Investigating the INoPs of other groups of English language learners in English-medium institutions will help scholars, educators, and students better understand the often unseen but vital social processes that mediate learning and consider ways of maximizing the potential of social networks and practices for their own educational purposes.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 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".