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Record W2796643502 · doi:10.5553/elr.000098

Aboriginal Title and Alternative Cartographies

2018· article· en· W2796643502 on OpenAlexafffundabout
Kirsten Anker

Bibliographic record

VenueErasmus Law Review · 2018
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsMcGill University
FundersMcGill University
KeywordsIndigenousOrder (exchange)Capital (architecture)Set (abstract data type)State (computer science)Focus (optics)Property (philosophy)ColonialismSociologyGeographyAestheticsEpistemologyLawPolitical scienceComputer scienceArchaeologyArtPhilosophyAlgorithmEconomics

Abstract

fetched live from OpenAlex

Aboriginal Title and Alternative Cartographies Indigenous claims have challenged a number of orthodoxies within state legal systems, one of them being the kinds of proof that can be admissible. In Canada, the focus has been on the admissibility and weight of oral traditions and histories. However, these novel forms are usually taken as alternative means of proving a set of facts that are not in themselves “cultural”, for example, the occupation by a group of people of an area of land that constitutes Aboriginal title. On this view, maps are a neutral technology for representing culturally different interests within those areas. Through Indigenous land use studies, claimants have been able to deploy the powerful symbolic capital of cartography to challenge dominant assumptions about “empty” land and the kinds of uses to which it can be put. There is a risk, though, that Indigenous understandings of land are captured or misrepresented by this technology, and that what appears neutral is in fact deeply implicated in the colonial project and occidental ideas of property. This paper will explore the possibilities for an alternative cartography suggested by digital technologies, by Indigenous artists, and by maps beyond the visual order.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.903
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.004

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 head0.037
GPT teacher head0.438
Teacher spread0.401 · 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; both teacher heads agree on what is shown here.

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

Citations6
Published2018
Admission routes3
Has abstractyes

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