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Record W1798911260 · doi:10.22230/jem.2013v14n2a550

Social Licence in British Columbia: Some Implications for Energy Development

2013· article· en· W1798911260 on OpenAlexaffabout
Fred L. Bunnell

Bibliographic record

VenueJournal of Ecosystems and Management · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Law and Evidence
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCommonwealthStakeholderPublic landPolitical scienceLaw

Abstract

fetched live from OpenAlex

Crown land is unique to the Commonwealth and better represented in British Columbia than anywhere else in the Commonwealth (95% of the land base). Through tradition and common law, British Columbians have come to define Crown lands as publicly owned lands that belong to all residents and to expect governments to shepherd them for the benefit of all. Social licence to operate on this land requires approval from the local community and other stakeholders. The concept of Crown land makes every British Columbian a potential stakeholder and has led to more drama and noise around social licence than occurs elsewhere. The four main reasons for failure in past applications for social licence have been a lack of respect, assuming economics is a sufficient framework, appearing to bully, and hiding or obscuring information deemed relevant. Recent events in the province suggest the provincial and federal governments, and some companies, have learned little from past failures. Energy development faces particular challenges because location counts and impacts are both intrusive and extensive, but the errors described here are avoidable. W. Edwards Deming reputedly observed, “Learning is not compulsory… neither is survival.” Some companies have learned.The topic is addressed under six headings: (1) Whose land is it?; (2) What the public has said; (3) Defining social licence; (4) Lessons from exploring social licence; (5) Lessons and energy development; and (6) What’s next?

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)0.001
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.455
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.032
GPT teacher head0.292
Teacher spread0.261 · 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
Published2013
Admission routes2
Has abstractyes

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