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Grassroots Communities Resistance Against Megaprojects: A Perceptual Legitimacy Lens

2025· article· en· W4416005805 on OpenAlexaff
Margaux Maurel, Ari Van Assche

Bibliographic record

VenueAcademy of Management Proceedings · 2025
Typearticle
Languageen
FieldDecision Sciences
TopicConstruction Project Management and Performance
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsGrassrootsLegitimacyInterpretation (philosophy)PerceptionFace (sociological concept)RestructuringScholarshipSocial movement

Abstract

fetched live from OpenAlex

Some foreign-invested megaprojects have encountered significant grassroots resistance, while others face little opposition, despite clear social and environmental misconduct. International business scholarship does not explain why megaprojects with similarly destructive impacts (e.g., displacements, pollution) do not uniformly face grassroots resistance. This paper uses a legitimacy-as-perception approach to analyse how and when grassroots communities develop a collective grassroots resistance. Our framework provides a holistic interpretation of legitimacy as a collective social judgment, formed by aggregating individual cross-level and interrelated perceptions of the megaproject, firm, host, and home governments. It recognizes the non-monolithic nature of a community, portraying it as agentic entity with distinct perspectives. It contends that collective social judgment and social structures are intertwined, offering a nuanced understanding of the intricate relationship between grassroots communities and the evolving dynamics of legitimacy. Therefore, it enables a more nuanced understanding of the emergence of a collective response (of acceptance, ambivalence, resistance) against megaprojects led by foreign MNEs. Megaprojects do not exist in isolation. Thus, this integrated local approach calls for a comprehensive restructuring of systems and structures involving all stakeholders engaged in megaprojects.

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)0.003
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.633
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0020.001
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 head0.089
GPT teacher head0.367
Teacher spread0.278 · 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
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

Citations0
Published2025
Admission routes1
Has abstractyes

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