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Sharing the colonial burden: Treaty‐making and reconciliation in Hul’qumi’num territory

2012· article· en· W1918672937 on OpenAlexaffvenue
Brian Egan

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsTreatyColonialismContext (archaeology)Political scienceLawAntarctic treatySociologyGeographyArchaeology

Abstract

fetched live from OpenAlex

This article explores different understandings of reconciliation within the context of modern treaty making in British Columbia, focusing on the role of the BC treaty process in resolving the longstanding dispute between Aboriginal Peoples and the Crown over rights to land. Although the treaty process was created to reconcile competing interests in the land, Crown and Aboriginal negotiators often have contradictory understandings of how this reconciliation is to take place. Drawing on a case study of the Hul’qumi’num Peoples, a group of Coast Salish First Nations, I examine how different understandings and approaches to reconciliation impede progress at the treaty table. I conclude that progress towards treaty and reconciliation in this case will require coming to terms with the Hul’qumi’num territory's colonial history and geography, something that the current treaty process actively avoids, plus the crafting of a treaty agreement that allows for a more equal sharing of the burden that colonialism has created in this place. More particularly, meaningful reconciliation will require a fuller recognition of Aboriginal title and rights across the breadth of the territory and a commitment to meaningful compensation of Hul’qumi’num Peoples for the wrongful taking of their lands.

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)0.010
metaresearch head score (Gemma)0.016
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 score0.168
Threshold uncertainty score0.653

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.005
Science and technology studies0.0430.037
Scholarly communication0.0150.005
Open science0.0020.010
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0050.000

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 head0.013
GPT teacher head0.246
Teacher spread0.233 · 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

Citations35
Published2012
Admission routes2
Has abstractyes

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