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Record W3035256660 · doi:10.1515/zaa-2020-0017

First Nations Healing: From Traditional Medicine to Experimental Ethnopharmacology

2020· article· en· W3035256660 on OpenAlexaboutno aff
Thomas Efferth, Gladys Alexie, Kai Andersch, Mita Banerjee

Bibliographic record

VenueZeitschrift für Anglistik und Amerikanistik · 2020
Typearticle
Languageen
FieldMedicine
TopicBiomedical Ethics and Regulation
Canadian institutionsnot available
Fundersnot available
KeywordsTraditional knowledgeIndigenousAppropriationTraditional medicineMedicinal plantsMerge (version control)Developing countryEngineering ethicsMedicinePolitical scienceEconomic growthBiologyEngineeringComputer science

Abstract

fetched live from OpenAlex

Abstract Focusing on First Nations traditional medicine, we investigated whether traditional knowledge of medicinal plants can be validated by modern scientific methods of molecular and cellular pharmacology and whether this information is of value for improving current therapy options. Based on two projects on medicinal plants of the Gwich’in – a First Nations group on the Canadian North West Coast – we found that extracts from several plants traditionally used medically were able to kill tumor cells, including otherwise multidrug-resistant cells. Investigating medicinal plants from Indigenous communities raises questions about ownership, appropriation, and commercial use. At the same time, because of the intricacies of patent law, publishing scientific investigations on medicinal herbs represents an effective way to prevent biopiracy. Therefore, research cooperation between industrialized and developing countries, and between Western and non-Western knowledge systems will facilitate ethically sound ethnopharmacological research and merge a diversity of competencies and knowledges.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.877
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.078
GPT teacher head0.373
Teacher spread0.294 · 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 designNot applicable
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

Citations4
Published2020
Admission routes1
Has abstractyes

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