Once a broker, always a broker: Non-professional interpreting as identity accomplishment in multigenerational Italian–English bilingual family interaction
Bibliographic record
Abstract
Abstract This paper explores interpreting in three-generational Italian–English bilingual families as a complex language brokering activity. Recent studies approach non-professional interpreting as language brokering in which bilinguals (often children) interpret for non-bilinguals (adults) in institutional settings (Hall 2004; Valdés 2003). These studies focus on brokering between minority group ‘insiders’ and majority group ‘outsiders’. My research extends these approaches, focusing on brokering in Italian–English bilingual family meal-time conversations. Second-generation family members have served as interpreters for their parents in institutional contexts since migrating as children over fifty years ago. They extend this practice to the family context, brokering between first- and third-generation family members in two ways. Triggered interpreting occurs when speakers verbally request clarification or when second-generation family members perceive conversational sequence problems. Non-triggered interpreting is neither requested nor sequentially triggered. Second-generation family members report playing an intermediary role unifying flanking generations. They act to bridge perceived linguistic and cultural gaps between their Italian-dominant immigrant parents and their English-dominant Canadian/US-born children. Interpretation in multi-generational conversations is one way through which these bridging roles and identities are accomplished locally in mundane interaction. The analysis includes an examination of spontaneous conversational data and participants' metacommentary and retrospective accounts of language brokering.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".