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Record W4386707142 · doi:10.1017/9781009334051.003

Reconnaissance Discourses for Colonial Law

2023· book-chapter· en· W4386707142 on OpenAlexaffabout
George Pavlich

Bibliographic record

VenueCambridge University Press eBooks · 2023
Typebook-chapter
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsLawlessnessColonialismDominionIndigenousSettlement (finance)PopulationLawHistoryPoliticsRace (biology)SociologyEthnologyPolitical scienceGender studiesDemographyEcology

Abstract

fetched live from OpenAlex

This Chapter offers an analysis of two influential, intelligence reports – authored by military officers William Butler and Patrick Robertson-Ross. They were instructed in 1870 and 1872 respectively by the Dominion of Canada (North-West Territories Council) to reconnoitre the North-West Territories in preparation for colonial settlement. Both reporters identified Alberta’s supposed lawlessness as requiring colonial intervention. It describes the socio-politics implied by the reports, detailing their race-tinted assessments, and how they divided an assumed population into racially conceived forms of life. Replicating popular imperial supremacist opinions, the reports egregiously placed ‘white’ settlers at the pinnacle of evolutionarily imagined notions of social progress. Both relied on rumour to recommend that law be used to enforce relational orders conducive to colonial settlement. This was to be achieved without provoking wars against Indigenous peoples.

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)0.004
metaresearch head score (Gemma)0.006
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 score0.555
Threshold uncertainty score0.884

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0260.047
Scholarly communication0.0120.006
Open science0.0010.004
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0070.000

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 head0.040
GPT teacher head0.237
Teacher spread0.197 · 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 routes2
Has abstractyes

Explore more

Same venueCambridge University Press eBooks→Same topicCanadian Identity and History→French-language works237,207→