Grassroots Communities Resistance Against Megaprojects: A Perceptual Legitimacy Lens
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.006 | 0.038 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".