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Record W6980719888

Connection to Land and Sea at Erub, Torres Strait

2009· article· en· W6980719888 on OpenAlexaffabout

Bibliographic record

VenueDigital Library Of The Commons Repository (Indiana University) · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicPacific and Southeast Asian Studies
Canadian institutionsConcordia UniversityMcGill University
Fundersnot available
KeywordsIndigenousLand rightsEthnographyState (computer science)DocumentationJurisprudenceColonialismConnection (principal bundle)
DOInot available

Abstract

fetched live from OpenAlex

"In this paper we examine the relationship of indigenous, ethnological, and legal discourses in the definition of rights to land and sea among Torres Strait Islanders in northern Australia. In Australia, to a greater extent than in Canada or in any other settler state, the rules and customs of indigenous tenure systems are legally regarded as the source and test for state recognition of native title. The native claims process routinely depends on a combination of indigenous and anthropological documentation and testimony to formulate jurisprudence on the validity of claims. Hence, a three-cornered discourse - indigenous, ethnological, and legal - is shaping the emergent realities of property, boundaries, and territories in contemporary Australia. \n \n"Our presentation takes us first through a consideration of general perspectives that have been applied in recent years to understanding the connection of people -- and peoples -- to their lands and seas. Next, we turn to a brief ethnography of customary tenure at Erub (Darnley Island), in the East Torres Strait, and some aspects of the colonial legacy. Finally, we discuss the post-Mabo legal-political setting and consider the interaction of state 'law' and islander 'custom'."

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.219
Threshold uncertainty score0.436

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0050.004
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.008
GPT teacher head0.187
Teacher spread0.179 · 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

Citations0
Published2009
Admission routes2
Has abstractyes

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