An investigation into the loss and revitalization of First Nations languages in Manitoba: perspectives of First Nations educators
Bibliographic record
Abstract
Abstract The main purpose of this research is to utilize the perspectives of research participants to address language loss in First Nations schools in Manitoba. The current status of some Aboriginal [Indigenous] languages are considered to be endangered (Statistics Canada, 2016). Most may be lost if we do not address this critical state. Indigenous languages are vital to the culture and knowledge systems of Indigenous peoples. The Indigenous Languages Act (2019) is now legislation after having passed its third reading through parliament. This Act gives hope for much needed funding to implement strategies for retaining and revitalizing Indigenous languages. It is hoped that the Indigenous Languages Act will help to empower Indigenous people to promote the worldview that highlights the importance of the Indigenous cultures and languages. It is important to note that the colonization process has had the most detrimental effect on language loss (Kirkness, 1998). As Kirkness had indicated in her 1998 collection of talks and papers: The intergenerational impacts of the residential school era have seriously disrupted the transmission of Indigenous languages. The power of government systems imposed over Indigenous peoples has also severely affected the retention of Indigenous languages. These impacts have resulted in the last two to three generations of families no longer speaking their Indigenous languages. Documents and reports such as Wahbung: Our Tomorrows (1971), First Nations Control of First Nations Education (2010), and The Truth and Reconciliation Commission of Canada (2015) with its ninety-four calls to action, emphasize the importance of language retention. Books, journals, and articles by Indigenous authors such as Tuhiwai-Smith, Kirkness, Gehl, Wilson and recent dissertations by Indigenous authors, Okemaw (2019), Fontaine (2018), Scott (2017), Murdock (2016), and Peden (2011), and non-Indigenous allies, Shackel (2017), Smith (2013), Arnett and Mady (2013) all contribute to the information on the impact of colonization and recommendations for addressing these impacts so Indigenous peoples can achieve mino pimatis(z)iiwin (journey of good life). Keywords: colonization, decolonization, Indigenous people, language loss, language retention, language revitalization, mino pimatis(z)iiwin, Anishinaabe, Anishinaabemowin, Ojibway, Aboriginal
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.035 | 0.013 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| 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".