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Record W4231387529 · doi:10.4324/9781003037163-2

Landscape citizenships

2021· book-chapter· en· W4231387529 on OpenAlexaboutno aff
James Bird, Ange Loft, Jane Wolff

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldArts and Humanities
TopicCultural Heritage Management and Preservation
Canadian institutionsnot available
Fundersnot available
KeywordsGeography

Abstract

fetched live from OpenAlex

Landscape citizenship in Canada is neither abstract nor uncomplicated. The country is famous for welcoming immigrants and refugees, but its settler colonial culture has disenfranchised and dispossessed the Indigenous peoples who have been here since time immemorial. So what does it mean to belong here? This chapter documents a conversation about landscape citizenship among three Canadians who met because of their attachment to the landscapes of Toronto. James Bird, a carpenter, cabinetmaker, and architecture student, belongs to the Nēhiyawak and Dënesųłiné Nations and is affiliated with the Northwest Territory Metis Nation. Ange Loft is an interdisciplinary performing artist and initiator from Kahnawake Kanienkehaka Territory. Jane Wolff is a cultural landscape scholar who came to Canada from the United States. Their discussion gave rise to wide-ranging questions, subjects, and insights about the importance of rootedness; the excitement of movement; the problem of exclusion; the need for ritual and ceremony; the presence of the sacred; the liveliness of language; the knowledge in names; and the power of kindness and care to engender meaningful relationships among people and with places.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.963
Threshold uncertainty score0.337

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0210.007
Scholarly communication0.0080.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0190.002

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 head0.158
GPT teacher head0.219
Teacher spread0.061 · 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

Citations5
Published2021
Admission routes1
Has abstractyes

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