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Record W4406101119 · doi:10.3390/mining5010004

Social Acceptability of Critical and Strategic Minerals (CSMs) Development: A Systematic Review with a Particular Focus on Quebec, Canada

2025· review· en· W4406101119 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueMining · 2025
Typereview
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsUniversité Laval
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaFonds de recherche du Québec
KeywordsContext (archaeology)Corporate governanceSystematic reviewCritical success factorPublic relationsPolitical scienceBusinessEnvironmental resource managementProcess managementGeographyMEDLINEEconomics

Abstract

fetched live from OpenAlex

The social acceptability (SA) of mining projects is a crucial issue for the sustainable development of territories and local communities. This article aims to identify the issues and examine the factors influencing the SA of exploration, exploitation and processing projects of critical and strategic minerals (CSMs) in Quebec. A systematic review guided by the PRISMA approach was conducted, selecting 57 relevant documents after excluding several others. The results reveal that between 2000 and 2024, the publication of studies on SA in the mining sector has increased significantly, particularly since 2013. Research is mainly concentrated in North America, with a predominance of case studies. Their critical analysis indicates that the SA of mining projects is based on various issues, including the need for greater consideration of the perspectives of local communities, as well as open and inclusive communication between all stakeholders. However, SA is affected by various factors, including demographic, economic, environmental and governance, which can have positive or negative effects depending on the context. Following this analysis, future research should explore the complex interactions between these factors, determining how some may act as mediators or moderators in various contexts through real case studies.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.057
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
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 head0.030
GPT teacher head0.277
Teacher spread0.247 · 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