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Record W4405293082 · doi:10.1139/facets-2024-0083

Does regulation delay mines? A timeline and economic benefit audit of British Columbia mines

2024· article· en· W4405293082 on OpenAlexafffundvenueabout
Rosemary‐Claire Collard, Jessica Dempsey, Youssef Al Bouchi, Nathan Bawaan

Bibliographic record

VenueFACETS · 2024
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsUniversity of British ColumbiaSimon Fraser University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsTimelineAuditBusinessGeographyAccountingArchaeology

Abstract

fetched live from OpenAlex

Seeking to capitalize on a surge in global demand for critical minerals, the Canadian mining sector claims that regulatory processes like Environmental Assessment (EA) impede and delay mining’s economic benefits. This paper investigates whether regulation has delayed mining projects and how much economic benefit mines have delivered in British Columbia (BC), focusing the mines’ performance post-EA. We audit the 27 mines granted an EA certificate in BC since 1995 and projected to open by 2022, comparing each mine’s forecasted and actual timelines and economic benefits (production, employment, and taxes), and identifying publicly-stated reasons for any mine delays. Seven of the 27 mines opened on time: 13 remain non-operational, and of the 14 mines that have operated, seven were delayed. Regulation was cited as a factor in only three of the 20 delayed projects; economic factors like commodity prices were the most common cause of delay. Lack of data and transparency on economic benefits significantly constrained our benefit audit, but BC mines for which data are available are underperforming across production (−77%), employment (−82%), and tax revenue (−100%). These findings suggest economic underperformance and mine delays post-EA are common, with delays typically resulting from economic factors, not government regulations.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.399
Threshold uncertainty score0.465

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 studies0.0000.000
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 head0.004
GPT teacher head0.179
Teacher spread0.175 · 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 designNot applicable
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

Citations6
Published2024
Admission routes4
Has abstractyes

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