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Record W2911488158 · doi:10.1139/cjb-2018-0195

Inuit plant use in the eastern Subarctic: comparative ethnobotany in Kangiqsualujjuaq, Nunavik, and in Nain, Nunatsiavut

2019· article· en· W2911488158 on OpenAlexafffundvenueabout
Alain Cuerrier, C. B. Clark, Christian H. Norton

Bibliographic record

VenueBotany · 2019
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsEspace pour la vie
FundersSocial Sciences and Humanities Research Council of CanadaHealth CanadaArcticNetParks Canada
KeywordsSubarctic climateEthnobotanyBiologyTaxonVascular plantEcologyGeographyBotanyMedicinal plants

Abstract

fetched live from OpenAlex

Plants are important in traditional Inuit life. They are used for food, tea, medicine, etc. Based on semi-structured interviews with 35 informants, we documented and compared plant names and uses in Kangiqsualujjuaq, Nunavik, and in Nain, Nunatsiavut. Plant names and uses were expected to be similar between communities owing to common boreal–subarctic environments and cultural ties. Both communities reported the same number of taxa, with equivalent proportions of vascular and nonvascular plants, growth forms, use categories, and medicinal uses. Forty-three species were used in each community, for a total of 78 species from 39 families. Despite a high overlap in species distributions, only 35% of nonvascular and 56% of vascular species were used in both communities. Correspondence was higher at the family level (64% of nonvascular and 75% of vascular families shared). The Ericaceae family was the most used, followed by Rosaceae. Thirteen of 30 medicinal species were shared between communities. There was a low correspondence regarding the conditions for which the medicinal species were used. Edible taxa were shared the most (52%). Plant uses unique to either Nain or Kangiqsualujjuaq may reveal separate bodies of traditional knowledge, or may reflect an overall loss of ethnobotanical knowledge in the Subarctic due to recent lifestyle changes.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.700
Threshold uncertainty score0.968

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.081
GPT teacher head0.373
Teacher spread0.292 · 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 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

Citations12
Published2019
Admission routes4
Has abstractyes

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