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Record W7018705906

Documenting Inuit knowledge: plants & their uses in Greenland

2013· other· en· W7018705906 on OpenAlexaboutno aff

Bibliographic record

VenueAmericanae (AECID Library) · 2013
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsSettlement (finance)ArcticTraditional knowledgePlant speciesDanishThe arctic
DOInot available

Abstract

fetched live from OpenAlex

While the native language of west Greenland, Kalaallisut, is robust with over 50,000 speakers, traditional knowledge of plant uses has been lost due to extensive Danish contact. We take an interdisciplinary approach to reconstructing this lost knowledge: the biologist provides botanical identification, plant uses, methods of collection, preparation, and storage, while the linguist provides access to the linguistic identification of the plants, both in Greenland and in a pan‐Inuit context, and access to the historical documentation. This collaborative effort allows us to document the revitalization of knowledge, reconstructed via exchange with other Inuit plant users (in Alaska and Canada) as well as other Arctic users. Here we report our fieldwork collecting the knowledge (linguistic, scientific and local) about plants in South Greenland. Our findings indicate that local knowledge of plant uses is greater than believed. Certain plants appear to be known across the population. In the August of 2011 we interviewed residents of two communities in Southern Greenland: Qassiarsuk (61°09′00′′N 45°31′00′′W), a sheep farming settlement of 60 people accessible only by boat, and Nanortalik (60°08′24′′N 045°13′54′′W), a town of 1500 with a helipad. Participants were identified by asking locals if they knew about plants, and if not, knew someone who did. In both communities all consultants except one were identified as knowledgeable by multiple parties. Interviews consisted of two parts. Participants were shown pictures and/or fresh or dried samples of plants and asked if they knew the plant and if there was a use for it. Often consultants harvested fresh specimens in preparation for our interview. All consultants showed us materials dried for personal use. For the second part of the interview to document differences in dialects and elicit information about uses, participants were recorded stating plant names from a database of 54 photos on an Apple iPad. Linguistic analysis of the Kalaallisut common names for plants show that a great majority of them fall into a relatively small set of morphosemantic categories, with many names semantically deriving from some specific visible or tactile property of the plant itself, as in the following examples: (1) Color: sungaartoq ‘yellow’ < sungaq ‘bile’ Papaver radicatum sungaartorsuaq ‘big yellow one’ Ranunculus acris L. sungaartuaraq ‘small yellow one’ (2) Resemblances: the suffix –usaq objects: Rhododendron groenlandicum qajaasaq < qajaq ‘kayak’ {leaf resembles a kayak} animals: Eriophorum scheuzeri ukaliusaq < ukaleq ‘hare’ {flower resembles a hare’s tail}

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.361
Threshold uncertainty score0.717

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0070.004
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.245
Teacher spread0.229 · 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 designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2013
Admission routes1
Has abstractyes

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