Placelessness through children’s literature
Bibliographic record
Abstract
Where do you belong? This seemingly simple question can be answered very differently by individuals of different ages. What is the developmental age when we find the answer to this question? What factors can influence our answer to this question? Answering this question or just feeling we are being questioned about it can contribute to feelings of placelessness. This can be especially difficult for the immigrant population (Schwartz et al., 2011; Syed & Juang, 2014). Additionally, since the place we live can act as a significant marker of identity (Corcoran, 2002), determining how they are connected to the place can also have an effect on a child's sense of culture and belonging. This qualitative study addresses the lacuna of research focused on the pedagogical practices teachers can use to enhance the voices of immigrant children through new understandings of place attachment in school immigrant populations and shows how schools can be supportive of children’s expression of culture and community. The study will explore how arts-based pedagogical practices and children’s literature can help to enhance students' identities and voices through their shared place-based narratives. Furthermore, it will explore challenges and successes with the multimodal expressions of immigrant children's voices in a primary classroom in an Eastern Canadian school with a diverse population.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.023 | 0.034 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".