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Record W3208238296 · doi:10.3390/ijgi10110749

Mapping Inuinnaqtun: The Role of Digital Technology in the Revival of Traditional Inuit Knowledge Ecosystems

2021· article· en· W3208238296 on OpenAlexfundaboutno aff
Brendan Griebel, Darren Keith

Bibliographic record

VenueISPRS International Journal of Geo-Information · 2021
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
FundersArctic Institute of North AmericaSmithsonian Institution
KeywordsOntologyMeaning (existential)Digital ecosystemArcticSociologyKnowledge managementEnvironmental resource managementComputer scienceEcologyEpistemology

Abstract

fetched live from OpenAlex

The term Inuinnaqtun is often used in reference to a dialect of Inuktut spoken by Inuinnait (Copper Inuit) of the Central Canadian Arctic. The broader meaning of Inuinnaqtun, however, is to speak, to create, to practice, to do, to think, to be, like an Inuinnaq (a human being). Inuinnaqtun was once its own robust ecosystem, with Inuinnait physically immersed in a landscape and way of life that nourished a fluent and full language, supported human relationships, and maintained a sophisticated body of cultural knowledge. The Inuinnait journey into the 21st century has challenged the practice of Inuinnaqtun, along with the connectivity of its ecosystem. How can an integrated Inuinnaqtun ecosystem be restored in contemporary Inuinnait society? In this paper, we outline the decade-long development of a digital mapping program to document traditional forms of engagement between Inuinnait people, language and land, and facilitate the continued circulation of knowledge that underlies these relationships. In reviewing its various successes and challenges, we critically question digital technology’s ability to digitally represent Inuinnaqtun ontology, in addition to the role that digital technologies can play in facilitating the local relocation of knowledge, objects and relationships dispersed into global contexts.

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)0.001
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 score0.303
Threshold uncertainty score0.227

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.025
GPT teacher head0.322
Teacher spread0.297 · 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

Citations11
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueISPRS International Journal of Geo-InformationSame topicIndigenous Studies and EcologyFrench-language works237,207