MétaCan
Menu
Back to cohort

Language, Identity, and Community Control

2007· book-chapter· en· W2502125850 on OpenAlexaffabout
Kate Hennessy, Patrick Moore

Bibliographic record

VenueIGI Global eBooks · 2007
Typebook-chapter
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsIdentity (music)IdeologySociologyLinguisticsPolitical scienceLawAesthetics

Abstract

fetched live from OpenAlex

To all my children, we are losing our language. You are our future leaders; you must learn our language. It is the root and heart of our culture. I pass you our language. You must learn our language. — “A Message to our Children,” Tagish First Voices Web site. From the turn of the century into the early 1970s, the Choutla Anglican residential school at Carcross in the Yukon Territory was home to generations of Tagish and Tlingit children. Victims of an assimilationist educational ideology that separated them from their families for at least ten months of the year, many children were denied the teachings of their elders, the right to speak their native language and, as a result, many aspects of their identity as native people. The Tagish and Tlingit community at Carcross has since come to terms with the pain and loss associated with the Choutla school and has become empowered to move beyond the extreme paternalism of the residential school era to greater self-determination and a deep sense of cultural identity. It is symbolic that in the very place where the native languages were aggressively decimated by the residential school policies, members of the local community are taking control of information technology to ensure the revival of the Tagish language. Control over technology has in this case facilitated the assertion of authority over every way their language is represented and made it possible for their cultural values and practices to define the nature of such representations.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.659
Threshold uncertainty score0.971

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.091
GPT teacher head0.452
Teacher spread0.361 · 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 designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations2
Published2007
Admission routes2
Has abstractyes

Explore more

Same venueIGI Global eBooksSame topicMultilingual Education and PolicyFrench-language works237,207