The Chameleon Character of Multilingual Literacy Portraits: Re-Searching in “Heritage” Language Places and Spaces
Bibliographic record
Abstract
The portrait of Canada as a welcoming place for immigrants can be traced to different immigration waves and political climates. Human geographer Soja’s (1996) concept of “third space”—a new space between collectives and individuals and historical periods-offers possibilities for exploring relationships among spaces, identity politics, and heritage languages. To understand children’s locations in multiple spaces, we draw on the Lefebvre/ Soja tradition within the theoretical landscape of critical human geography. Relevant here are Lefebvre’s (1991) three different kinds of spaces: espace perçu-perceived physical space, espace conçu-conceived, mental, or imagined space, and espace veçu-third space as directly lived through social practices. Conceptualizing children’s identity construction as a recursive process necessitates a double perspective-looking at local literacy moments in their daily living and the more global, political discourses in which they may be located. To articulate a vision of diverse spaces where locations of possibility are open for children to “speak” and “be,” we draw on Bakhtin’s (1990) dialogic concept of self, which creates an active response to the utterances of individuals, their social and temporal worlds. We use chameleon as a metaphor to characterize our attempts to draw portraits of multilingual children’s literacy practices and identity construction in diverse spaces. We also use it in the postmodern sense to signal the paradoxical, elusive nature of identity in global times as fluid and discursively constructed in ways that are not always visible, easily recognizable, or politically and personally valued. Yon (2000) maintains that diaspora “as a theoretical concept…helps us to think about culture and cultural processes as forged through transnational networks and identifications” (pp. 17-18). Braziel and Mannur (2003) argue that “Diaspora remains, above all, a human phenomenon-lived and experienced” (p. 8).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.018 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".