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Record W3027523662 · doi:10.1017/s0032247420000170

The knowledge that went up in smoke: Reindeer herders’ traditional knowledge of smoked reindeer meat in literature

2019· article· en· W3027523662 on OpenAlexfundno aff
Kia Krarup Hansen, Turid Moldenæs, Svein Disch Mathiesen

Bibliographic record

VenuePolar Record · 2019
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
FundersNorges ForskningsrådArctic Institute of North America
KeywordsGeographySociology of scientific knowledgeEthnologySociologySocial science

Abstract

fetched live from OpenAlex

Abstract Using a literature review, this paper defines the knowledge status of smoked reindeer meat and investigates to what degree reindeer herders’ traditional knowledge has been included in scientific articles and grey literature. We developed a four-level categorisation of the degree of including traditional knowledge, from “non-participation” to “self-determination,” and three levels of focus. Very few scientific articles on smoked or smoking reindeer meat appeared in the review. Not only did reindeer peoples’ traditional meat smoking knowledge “went up in smoke”—both literally and metaphorically—but also incorrect conclusions were often drawn as a result of that exclusion. We argue that reindeer herders’ traditional knowledges and practices of smoking reindeer meat need examination and inclusion through co-production or self-determination methods across scientific disciplines.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.029
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0090.007
Science and technology studies0.0010.004
Scholarly communication0.0050.005
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.073
GPT teacher head0.356
Teacher spread0.283 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2019
Admission routes1
Has abstractyes

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