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Record W3088760806

The role of archives in Indigenous language maintenance and resurgence

2020· dissertation· en· W3088760806 on OpenAlexaboutno aff
Samantha E. Booth

Bibliographic record

VenueMspace (University of Manitoba) · 2020
Typedissertation
Languageen
FieldArts and Humanities
TopicDiverse Musicological Studies
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousIndigenous languageLinguisticsBiologyPhilosophyEcology
DOInot available

Abstract

fetched live from OpenAlex

For centuries, Indigenous peoples have been advocating for their rights to their land, cultures and languages in the context of (settler) colonial institutions that have repressed and removed these rights and knowledges, as well as the mechanisms for their transmission. This thesis attempts to open up questions regarding what settler-colonial archives and archivists could do to support Indigenous language maintenance, resurgence and use, given the reality that most Indigenous languages in Canada (and globally) are declining in use and number of speakers. Using Inuktut (Inuit languages) as a case study, it will outline the circumstances that have led to both this decline and the role that settler-colonial archives have had in it. By examining Inuktut records held by the settler-colonial institution of Hudson’s Bay Company Archives (HBCA) and their Names and Knowledge Initiative as a case study, this thesis will illustrate both the challenges posed by Indigenous language records held by such institutions, as well as the opportunities for (settler) colonial archives to contribute to Indigenous sovereignty over their linguistic data, knowledge and records. It will also explore the use of Indigenous languages in the delivery of services by archives to further support their use as languages of daily life.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.589
Threshold uncertainty score0.739

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.186
Teacher spread0.165 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2020
Admission routes1
Has abstractyes

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