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Record W4414291640 · doi:10.4324/9781003488507

Contested Consultations in the Extractive Industries

2025· book· en· W4414291640 on OpenAlexaboutno aff
Paul Alexander Haslam, Nathan Andrews, Karin Buhmann, Ibironke T. Odumosu-Ayanu, Mark C. J. Stoddart

Bibliographic record

Venuenot available
Typebook
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsnot available
Fundersnot available
KeywordsCONTESTStakeholderIndigenousCorporate governanceNormativeDemocracyBest practice

Abstract

fetched live from OpenAlex

In recent years international organisations, governments and companies around the world have dramatically reformed the regime that governs consultations with community stakeholders about proposed extractive projects. However, the characteristics of this consultation regime are often contested, with diverse stakeholders seeking to defend their interests by drawing on different authoritative interpretations of the rules, norms and decision-making procedures that govern stakeholder consultation. Contestation over the meaning, governance and practice of stakeholder consultation is the central thread that ties this book together. Within this overarching concern, the volume takes a global and comparative perspective that examines the complexity of these intersecting and overlapping consultation requirements, with a particular focus on Indigenous Peoples, using cases from the Global North and Global South, including Argentina, Australia, Brazil, Canada, The Central African Republic, The Democratic Republic of Congo, Iceland, Ghana, Greenland, Guyana, Norway, and Peru. The book highlights the tensions associated with the application of this contested regime and identifies possible solutions from best practices around the world. From a theoretical perspective the book unpacks the maze of overlapping consultation requirements and practices that highlights the normative disagreements between key stakeholders and the overlapping rules and procedures that govern the implementation of consultation. A unique contribution of this collection is the commentary from practitioners, who reflect on the same issues addressed by the academic contributors, but based on their own vast practical experience.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.013
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0130.023
Scholarly communication0.0100.008
Open science0.0010.007
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0070.001

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 head0.224
Teacher spread0.206 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2025
Admission routes1
Has abstractyes

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