Bibliographic record
Abstract
This article analyses theatre project ‘Hul’q’umi’num’ Heroes: Reclaiming Language through Theatre’, which aims to stem the decline of the Hul’q’umi’num’ language by bringing traditional stories about heroes to life in dramatic performances that spark and hold the interest of language speakers, language learners, and the general public. The territory of the Hul’q’umi’num’ people extends along the Salish Sea from Nanoose to Malahaton Vancouver Island in British Columbia, Canada. Today, around forty fluent first-language speakers remain, mostly over the age of 60, and thus Hul’q’umi’num’ is considered an endangered language. However, among the population of over 6,000 Hul’q’umi’num’, there are many people who desire to learn the language or improve their fluency. By embarking on the next step—turning traditional stories into theatre—the wproject is hoping to bring the language to the eyes and ears of the community, and, for the participants, it will possibly help unlock their ability to speak Hul’q’umi’num’. This article argues that applied theatre can be used to effectively address concerns pertaining to—in this case—the indigenous community the language revitalization work is meant to benefit, even though there are multiple challenges that stand in the way of such a process. The work has a strong focus on traditional and playful artistry and on consensus.
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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.052 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 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".