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Record W2110638890 · doi:10.7202/013933ar

“When our words are put to paper.” Heritage documentation and reversing knowledge shift in the Bering Strait region

2006· article· en· W2110638890 on OpenAlexvenueno aff
Igor Krupnik

Bibliographic record

VenueÉtudes/Inuit/Studies · 2006
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsDocumentationIndigenousTraditional knowledgeSubsistence agricultureCultural heritageGoodwillIntangible cultural heritageCircumstantial evidenceCultural heritage managementIndustrial heritageEnvironmental ethicsSociologyPolitical sciencePublic relationsGeographyBusinessArchaeologyAgricultureEcology

Abstract

fetched live from OpenAlex

The paper examines the relationship between indigenous knowledge and heritage documentation efforts generated by scientists and other forms of local activities that work in strengthening indigenous cultural identity and tradition. As the studies in indigenous heritage and environmental knowledge have become one of the fastest-growing fields in northern cultural research, there is tough competition for limited resources and, even more, for the time, goodwill, and attention of northern constituencies. Scholarly projects in heritage and knowledge documentation represent just one stream within today's public efforts, though an important and visible one. Those projects do have an impact in local communities; but such impact is often subtle, circumstantial, and may not be sustainable when left standing on its own. Local knowledge, very much like active language, relies primarily on oral transmission, family ties, community events, and subsistence activities. As long as those prime channels of cultural continuity are working, “our words put to paper”—knowledge and heritage sourcebooks, school materials, and catalogs—should be regarded as long-term cultural assets that may play a crucial role in the transformed northern societies of today and of tomorrow.

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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.915
Threshold uncertainty score0.998

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 communication0.0000.000
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 head0.072
GPT teacher head0.399
Teacher spread0.327 · 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

Citations10
Published2006
Admission routes1
Has abstractyes

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