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Record W4223455788 · doi:10.1080/15230430.2022.2049957

On the role of peat bogs as components of Indigenous cultural landscapes in Northern North America

2022· article· en· W4223455788 on OpenAlexafffundabout
Jeffrey Speller, Véronique Forbes

Bibliographic record

VenueArctic Antarctic and Alpine Research · 2022
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsMemorial University of Newfoundland
FundersInstitute of Social and Economic Research, Memorial University of Newfoundland
KeywordsBogPeatIndigenousGeographyPlateau (mathematics)EcologyThe arcticPhysical geographyArchaeologyGeologyOceanographyBiology

Abstract

fetched live from OpenAlex

This article explores uses of peat bogs and associated plants and other resources by drawing on the published ethnobotanical and archeological literature pertaining to Indigenous groups that lived and continue to live on the Northwest Coast, the Interior/Plateau Regions, Northwestern Canada, the Central and Western Arctic, and the Far Northeast. We examine bog plants used as food and medicine, the relationships between people and bogs as documented through traditional ecological knowledge, and archeological evidence for bogs having been used as places to live and as sources of peat for use as building material. The aim is to bring attention to the fact that peat bogs were, and still are, very much a part of Indigenous cultural landscapes in North America. We suggest that greater attention should be paid to bogs and that a reassessment of their perceived marginality may be necessary to achieve a fuller understanding of past and present human–environment interactions in northern North America.

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)0.001
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.835
Threshold uncertainty score0.328

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.006
Scholarly communication0.0020.002
Open science0.0000.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.049
GPT teacher head0.378
Teacher spread0.329 · 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 designQualitative
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

Citations7
Published2022
Admission routes3
Has abstractyes

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