Exploring the Boundaries of Emotion and Language: Elementary School Newcomers with Emerging Print Literacy as Co-researchers
Bibliographic record
Abstract
The participants in this study are students in Grades 4 to 6 who arrived in Canadian elementary schools with emerging print literacy not having had the right to learn to read and write before migrating to Canada. The research questions that guide this study are: Who are newcomers with emerging print literacy in elementary schools? What emotions are associated with and shape the students’ identities and literacy practices? And how do the students understand the relationship between these emotions and their identities and literacy practices? To answer these questions, this research combines the theories of translanguaging, which views language as being socially constructed and complex (García & Wei, 2014); identity as social positions through which the self is developed (Hall, 1996; Moje, Luke, Davies & Street, 2009); and the sociality of emotion where emotions create the boundaries of what a person or idea is (Ahmed, 2004). Employing a humanizing methodological design (Paris, 2011), I utilized qualitative critical case studies to work with the students in co-creating oral and written texts as well as co-researching with the students to identify and analyze themes of emotions found in each-other’s texts. Each of the three case studies took place at a different school site and included four to six students and their teachers. Key components of the students’ identities are plurilingualism, racialization, low socio-economic status, English-only writers, avid video-game players and social media users, regional migrators and sophisticated consumers of various cultural experiences. The student identified themes include love of family and friends, safety in religion, anger with writing and shyness about videos, which were interwoven with other themes from the data including trauma, print literacy humiliation and safety in Canada. This research shows how plurilingual children who are in the process of learning to read and write can participate meaningfully in the research process. Emotions and translanguaging combine to take us deeper into understanding how the pedagogical incorporation of the students’ plurilingualism and previous experiences, especially with family, are essential to a classroom without humiliation that is truly culturally sustaining and trauma-informed.
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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 | 0.019 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.021 | 0.025 |
| Scholarly communication | 0.022 | 0.011 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.003 | 0.008 |
| 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".