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Record W2726154201

Giving back to the language community: lessons from the Kiowa and Ojibwe peoples

2017· article· en· W2726154201 on OpenAlexaboutno aff
Taylor Cole Miller

Bibliographic record

VenueThe COCOON platform (University of Paris) · 2017
Typearticle
Languageen
FieldComputer Science
TopicNatural Language Processing Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsCommitTribeLinguisticsHistoryComputer scienceSociologyAnthropologyPhilosophy
DOInot available

Abstract

fetched live from OpenAlex

In this paper, I discuss two very different language communities: the Kiowa tribe in southwestern Oklahoma and the Saulteaux Ojibwe first nation in Manitoba. Kiowa is a critically endangered language with only 10 native speakers, all nearing the age of 90. Revitalization efforts have been intermittent over the last 40 years, and little has helped. Saulteaux Ojibwe, while also endangered, is much healthier with 10,000 native speakers, some of whom are teachers with linguistic training actively conducting their own research. Drawing from my experience, I show that these marked differences lead to distinct needs and thus different directions for researchers to give back to the communities. I detail two general approaches that any linguist should be able to adopt in similar communities. The Kiowa tribe has little access to audio recordings of their language because the recordings were not digitized and/or copies were never provided. They have not seen a number of the resulting papers and books from previous researchers, and the only comprehensive dictionary is from the 1920s. In the words of one of my consultants: “If there was a dictionary with the sounds, too, I could die. I’m one of the last ones who knows how you’re supposed to pronounce things, and it will be lost forever without something like that.” While it is true that not everyone is equipped to commit to a full dictionary project, by doing research as we would anyway we can still make a big impact. Namely, a linguist can amass the available data in preparing their own research (search for previous recordings, contact other scholars, scan articles and books) and also be sure to give copies of those materials directly to the community (e.g. a museum, library, or community center). There are already online dictionaries and educational applications for the many dialects of Ojibwe, making research and data more readily available to the community and interested researchers. Thus, the community requires a markedly different approach than that of the Kiowa tribe. Namely, the Saulteaux Ojibwe community’s language teachers and researchers, working on the front lines of language revitalization, are seeking validation and support. In the spirit of more collaborative fieldwork (e.g., Mannix et al. 2015, Brooke Lillehaugen p.c.), we should look to work with the native community in the production and dissemination of the research (e.g. co-authoring papers, presentations, and grants), bringing our skills (and data) to the table of ongoing projects.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.386
Threshold uncertainty score0.999

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 studies0.0030.000
Scholarly communication0.0000.001
Open science0.0050.003
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 head0.034
GPT teacher head0.270
Teacher spread0.236 · 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.

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

Citations0
Published2017
Admission routes1
Has abstractyes

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