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Record W6942357988 · doi:10.14288/cl.vi257.197876

“Breathing forests and fields”

2023· article· en· W6942357988 on OpenAlexaboutno aff

Bibliographic record

VenueOpen Collections · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMycorrhizal Fungi and Plant Interactions
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousColonialismScholarshipDilemmaObligationPower (physics)NaturalizationTraditional knowledge

Abstract

fetched live from OpenAlex

Indigenous literary scholarship and teaching in the Canadian university often involves a simultaneous and sometimes conflicting obligation to foreground settler-colonial relations of power while also emphasizing sovereign Indigenous intellectual histories. I offer a discussion of two very different texts through which I have approached this dilemma in my own teaching practice: the Hudson’s Bay Company (HBC) advertisement for Vancouver 2010 winter Olympic-wear, “Timeline,” and Syilx writer and philosopher Jeannette Armstrong’s poem, “History Lesson.” While the HBC advertisement supplies an instructive example of settler-colonial tropes arranged according to a linear-progressive view of history and time, Armstrong’s free-verse poem storytells colonial exploitation and violence outside of traditional Western practices of chronology and the naturalization of progress narratives. That is, “History Lesson” centers Indigenous thought in its engagements with settler-colonial history and time, and asserts balanced and regenerative lifeways for the health of Indigenous futures.

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)0.003
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: Other
Teacher disagreement score0.657
Threshold uncertainty score0.691

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0250.046
Scholarly communication0.0090.005
Open science0.0010.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0100.001

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 head0.024
GPT teacher head0.246
Teacher spread0.222 · 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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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