“We Are Looking to Each Other, Waiting to Understand”: An Examination of Spatial Repertoires in Newcomers’ Communicative Encounters
Bibliographic record
Abstract
Research has underlined the importance of communicative competence in order for newcomers to succeed and build resilience in their adopted homeland. Studies on this topic, however, have generally assumed the primacy of linguistic competence as the principal locus of investigation without paying due attention to the nuances of how new immigrants use “spatial repertoires” – interactive assemblages of people, objects, language, and environment – in accomplishing day-to-day communicative goals. Part of a larger project that examined newcomer resilience, this paper reports on the findings drawn from five newcomer families in Canada regarding their assemblage of spatial repertoires to negotiate their communicative needs. Data were collected from semi-structured interviews, written artifacts, art-based multimodal activities, field observations, and reflective notes. Using spatial repertoires in multilingual communication as the conceptual framework, we found that conditions that allowed our participants the following affordances were conducive for deployment of spatial repertoires: (a) translations and information mining, (b) translanguaging, (c) identity affirmation, (d) collaboration and solidarity, and (e) integration of semiotic resources. The findings provide insights into how these newcomers demonstrated success in achieving their communicative goals despite various challenges experienced as non-native speakers of English. We conclude by discussing the implications of these findings.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.018 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".