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Record W3107937505 · doi:10.5382/sp.12.07

The Rise of Sustainability

2005· book-chapter· en· W3107937505 on OpenAlexaff
Alistair MacDonald, Ginger Gibson

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicEnvironmental and Social Impact Assessments
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSustainabilityEnvironmental scienceBusinessBiologyEcology

Abstract

fetched live from OpenAlex

Abstract The risks associated with, geographic locations of, and engagement strategies required by exploration companies have shifted in the last 25 years, largely in response to changing public attitudes and governance approaches. A mixture of growing global environmental consciousness, the rise of international civil society networks, and economic liberalization and government deregulation in the developing world have all played key roles in altering the environment for mineral exploration. Three fundamental tenets of a positive climate for exploration have been altered: access to land, ease and speed of permitting, and the right to mine deposits after discovery. This paper identifies the importance of new stakeholders, including nongovernmental organizations, international financial institutions, and community activist networks; new governance approaches, such as self-regulation, the use of international standards and co-management, and new monitoring and engagement techniques, such as environmental and social impact assessment, thirdparty monitoring, and prior informed consent. The traditional relationship focus between states and corporations has been replaced by constantly evolving relationships among corporations, governments, civil society, and affected communities. Exploration companies, particularly junior companies, must use available tools—early community engagement strategies and available guidance documents from industry associations among them—to adapt themselves to this new focus.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.871
Threshold uncertainty score0.988

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0130.000

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 head0.254
Teacher spread0.246 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations9
Published2005
Admission routes1
Has abstractyes

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