Documenting Inuit knowledge: plants & their uses in Greenland
Bibliographic record
Abstract
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}
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.034 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".