Language Reawakening Through Theatre
Bibliographic record
Abstract
Already challenged by the ravaging effects of colonisation over the past two centuries, Indigenous languages in Canada continue to face erasure due to the impacts of neo-liberalism and globalisation. Consequently, a high priority has been placed on the preservation of languages through ongoing research and language teaching. Here in Canada, despite the government’s financial and administrative aid for language awakening, reversing language loss has not yet been achieved. This chapter focuses on how applied theatre can support the cross-generational transfer of Indigenous languages and cultures as a community-based, participatory, and immersive tool to bring forward stories from page to stage and encourage discussion on critical social issues while speaking to the core elements of traditional culture such as storytelling, performance, and sharing knowledge. Moreover, this chapter discusses the urgency of utilising bottom-up and collaborative models of applied theatre via Indigenous and decolonising methodologies that work through reporting back to and sharing knowledge with (and for) Indigenous communities. The ethics of this anti-hierarchical and caring approach is equally significant in relation to the ethical foundations of Indigenous ontologies that contribute to the Indigenous people’s well-being and agency through respect, relevance, reciprocity, and responsibility.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".