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Towards an Indigenist data management program: reflections on experiences developing an atlas of sea ice knowledge and use

2011· article· en· W1726414725 on OpenAlexafffundvenueabout
Peter Pulsifer, Gita J. Laidler, D. R. Fraser Taylor, Amos Hayes

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2011
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsCarleton University
FundersLuonnontieteiden ja Tekniikan Tutkimuksen ToimikuntaOffice of Polar ProgramsNatural Sciences and Engineering Research Council of CanadaCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorGovernment of Canada
KeywordsIndigenousDocumentationContext (archaeology)Traditional knowledgeAtlas (anatomy)SociologyPolitical scienceGeographyComputer scienceMedicineBiologyArchaeology

Abstract

fetched live from OpenAlex

The International Polar Year (2007–2008) (IPY) established an IPY data policy that guides a formal data management (DM) process. The DM system envisioned includes data based on Indigenous knowledge systems linked to data collected in the Western scientific tradition. Based on experiences developing an online Atlas of Inuit Sea Ice Knowledge and Use (Siku Atlas) we argue that an ‘Indigenist’ DM program must be developed if the envisioned IPY DM system is to be realized. Existing ideas proposing an Indigenist research paradigm are discussed in the context of DM. To move towards the development and implementation of an Indigenist DM program, we review four key relationships for consideration when documenting Indigenous knowledge and managing the resulting data: Indigenous and Western scientific knowledge systems; communities and researchers; Indigenous knowledge and power; and Indigenous knowledge and documentation methods. To ground the discussion, we link Indigenist DM processes to the Siku Atlas development process and results. Last, lessons learned are presented along with an outline of directions for a research program in support of an Indigenist DM program .

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 categoriesMeta-epidemiology (narrow), Science 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 score0.128
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0070.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.118
GPT teacher head0.367
Teacher spread0.249 · 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

Citations55
Published2011
Admission routes4
Has abstractyes

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