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Record W2075608957 · doi:10.3138/utlj.0224

Resourceful impacts: Harm and valuation of the sacred

2014· article· en· W2075608957 on OpenAlexvenueaboutno aff
Sari Graben

Bibliographic record

VenueUniversity of Toronto Law Journal · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicEnvironmental Justice and Health Disparities
Canadian institutionsnot available
Fundersnot available
KeywordsHarmValuation (finance)AdjudicationProsperityContext (archaeology)DamagesEnvironmental ethicsSociologyLawPolitical scienceBusinessGeographyArchaeologyFinance

Abstract

fetched live from OpenAlex

The use of rationalized risk assessment to identify the costs and benefits of protecting Aboriginal sacred sites is ubiquitous in Canadian law. Like other contemporary critics of cost-benefit analysis, I voice concerns with its use to adjudicate moral claims and recognize that it can misidentify the depth of loss experienced by Aboriginal peoples when sacred sites are destroyed. Nonetheless, in this article, I question in what ways technocratic approaches to risk could be helpful in protecting sacred sites. The article draws on two recent environmental assessments, the Prosperity Gold-Copper Mine Project in British Columbia and the Screech Lake Uranium Exploration Project in the Northwest Territories, to argue that innovative approaches to characterizing loss illustrate the potential of rationalized methods to identify harm better than it has in the past. The panels’ recommendations to reject the projects, based on the risk that the communities would suffer mental and psychological harm, reflect a genuine effort to provide decision makers with the real cost of approving these two projects. While I do not suggest that cost-benefit analysis can represent the loss of absolute values, I argue that, if done with cultural context in mind, assessment may help to extract the type of information needed to find the depth of empathy from which legal solutions may be constructed.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.465
Threshold uncertainty score0.944

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 head0.019
GPT teacher head0.254
Teacher spread0.235 · 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 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

Citations5
Published2014
Admission routes2
Has abstractyes

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