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Record W1988661327 · doi:10.1177/1206331203251665

Landscaping Desire

2003· article· en· W1988661327 on OpenAlexaffabout
John Sandlos

Bibliographic record

VenueSpace and Culture · 2003
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsYork University
Fundersnot available
KeywordsWildernessThreatened speciesWildlifeDominionFrontierGovernment (linguistics)LegislatureGeographyEthnologyCommissionPolitical scienceEnvironmental ethicsHistoryArchaeologyLawEcologyHabitat

Abstract

fetched live from OpenAlex

There is a long tradition in the Canadian North of outsiders imagining the region in accordance with their own cultural assumptions. At the turn of the century, naturalists, hunters, and explorers such as Ernest Thompson Seton, Caspar Whitney, and G. H. Blanchet began to describe the North as a last wilderness frontier that was teeming with vast herds of caribou. Many of their narratives also described native hunters as “wanton” killers of wildlife who threatened the sanctity of the Northern wilderness; they argued for increased government legislative controls and, paradoxically, the controlled exploitation of caribou in ranches or organized hunts. This article argues that all of these images of the Northern caribou had a profound influence on the federal government's wildlife policy in the region through the early part of the 20th century. Indeed, the federal government's restriction of native hunting rights through legislative reform and their tentative efforts to establish reindeer and caribou ranches can be traced directly to the cultural representations of the Northern landscape that appeared in the earliest natural history surveys of the Canadian North.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.261
Threshold uncertainty score0.519

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.005
Scholarly communication0.0050.002
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0590.007

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 head0.031
GPT teacher head0.355
Teacher spread0.324 · 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

Citations4
Published2003
Admission routes2
Has abstractyes

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