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Record W6958778482 · doi:10.6093/2035-8504/8588

Land Rights in Mediatized Indigenous Legal Discourse

2021· article· en· W6958778482 on OpenAlexaboutno aff

Bibliographic record

VenueUniversità degli Studi di Napoli Federico II · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLeaf Properties and Growth Measurement
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousGovernment (linguistics)Indigenous rightsNegotiationHuman rightsRights of Nature

Abstract

fetched live from OpenAlex

In the years 2016-2018 a number of protests conducted by the Indigenous peoples of Canada against the controversial expansion of the Kinder Morgan pipeline was framed in the Canadian news discourse as a conflict involving the First Nations, the federal government and the provincial government of Alberta. The dispute over pipeline regulations, environmental risks and Indigenous land rights saw First Nations peoples arguing against the government of Canada and the government of Alberta as the new expansion would further aggravate water and air pollution on Indigenous sacred lands; while the Liberal Party’s leader and PM, Justin Trudeau, had promised to make environmental assessment credible again, the government approved plans to build pipelines on lands whose ownership is still hotly contested. Based on the assumption that the media acts as a proxy for personal contact with the legal system and that legal language plays an important role in the construction, interpretation, negotiation and implementation of legal justice, the present paper intends to investigate the mediatization of Indigenous Law, i.e. the construction and dissemination of legal knowledge on Indigenous land rights in online news discourse for global consumption.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.395
Threshold uncertainty score0.998

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)0.0000.000

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 head0.017
GPT teacher head0.209
Teacher spread0.191 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2021
Admission routes1
Has abstractyes

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