<i>Landguaging</i> Imperialism through Teacher-Reflection Art: Land-Sensitizing Tools for Imperial Language Instructors
Bibliographic record
Abstract
Land dispossession is key to imperialism as it enables settlers to deterritorialize from their homelands and reterritorialize onto foreign lands, displacing Indigenous inhabitants. In Canada, this settler colonial process not only imposed English and French as dominant languages but also contributed to a broader desensitization to land among their speakers. Ecolinguistics responds to this disconnection by fostering positive land/language relationships using plurilingual and land-sensitizing techniques (i.e., environmental attunement). Currently, many Canadian language teachers struggle to integrate these approaches into their pedagogies. Following teacher reflection research and arts-based inquiry, the Multimodal Autobiographical Landguaging Portrait (MALP) was developed to support imperial language teachers in connecting their language teaching and learning experiences to the land(s) upon which they occurred, attuning them to the role of land in language use. Qualitative analyses of eight MALPs from plurilingual pre-service ESL teachers in Quebec showed that land was understood anthropocentrically. However, those with knowledge of their Indigenous backgrounds or experience with Indigenous pedagogies demonstrated higher levels of environmental attunement. The MALP was useful for articulating land/language embodiment of heritage languages, Indigenous issues, and imperial expansionism, but it was less successful in making connections between English/French and their European homelands. Ecolinguistic activities are thus provided to support land-sensitizing pedagogies that promote positive environmental attunement.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".