Diverse Belongings: An Improvisational Inquiry Into Newcomer Worlds, Worldings, and the Literacies of Belonging
Bibliographic record
Abstract
In an era of unprecedented global forced displacement, this artistic, multimodal dissertation explores experiences of belonging with a group of four adult newcomers to Canada. Using a post qualitative approach, the study couples the theoretical concepts of worlding and wonder with the work of Borderlands poets — non-western authors who write from the margins — to explore the creative texts created by the bi- and multilingual English learners from a decolonial stance. The study’s setting, during the Covid-19 pandemic, was an online translanguaging space, in which the participants’ linguistic, artistic, and multimodal repertoires were leveraged in meaning- making and artmaking, including drawings, paintings, digital photography, video and dual language poetry. Poetic transcripts were generated to re-present the participants’ resettlement stories. The findings reveal how affective and resonant worldings emerged through the serial immersion in experiences of belonging, not-belonging, and deeply felt liminal spaces between- belongings. Unworlding stories exposed disturbing examples of the participants’ loss of voice, of silencing in dominant English spaces, even among newcomers with English language proficiency. This inquiry seeks to contest dominant forms of academic knowledge and expand creative approaches within the post-qualitative paradigm to open new avenues for creative inquiry in language, literacy, and arts-based research.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.025 | 0.036 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".