Identity Construction as Nexus of Multimembership: Attempts at Reconciliation Through an Online Intercultural Communication Course
Bibliographic record
Abstract
Using situated learning (Lave & Wenger, 1991 Lave, J. and Wenger, E. 1991. Situated learning: Legitimate peripheral participation, Cambridge, UK: Cambridge University Press. [Crossref] , [Google Scholar]) and communities of practice (Wenger, 1998 Wenger, E. 1998. Communities of practice: Learning, meaning, and identity, Cambridge, UK: Cambridge University Press. [Crossref] , [Google Scholar]) as our theoretical framework, we focused on two female graduate students in applied linguistics as each attempted to negotiate memberships in multiple communities during an international exchange program. Eleven students at six universities took part in an internet-based intercultural communication course in addition to courses at their host universities, generating data in the form of online postings, final course papers, e-mails to the instructor, and retrospective evaluations. Ines, a Mexican student in Canada, appeared to reconcile her identity successfully as a nexus of multimembership. Adrienne, a U.S. student living in Mexico, attempted to participate in practices at her host university but felt marginalized. Our analysis demonstrates the difficulty, complexity, and sometimes the impossibility of reconciliation as a process for weaving a nexus of multimembership into one identity when encountering new practices across national borders.
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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 | 0.029 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.019 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".