â"IT'S NOT MY STORY": THE DEVELOPMENT DISCONNECT BETWEEN CORPORATE SOCIAL RESPONSIBILITY AND THE NARRATIVES OF COMMUNITIES IMPACTED BY MINING IN PERU'S ANDESâ
Bibliographic record
Abstract
This thesis examines the disconnect between the stated intentions of mining companies and narratives of hegemonic dispossession from mining-affected communities in the Andean region of Peru. The study focuses on Barrick Gold Corporations’ operations in rural Peruvian communities to illustrate how policy decisions and corporate privilege in Canada, and globally, construct hegemonic processes of development broadly. The research question asks how the mining industry frames its intentions so that civil society in Canada subscribes to the interest of this elite group. Findings from two case studies in rural Peru show that the mining industry uses instrumental tools such as Sustainable Development (SD), Corporate Social Responsibility (CSR) and partnerships with NGOs to create an illusion of shared values with civil society. The presence of a transnational capitalist class (TCC) is evidenced by examples of collaboration between government and corporate efforts. I argue that a TCC enables global mining to maintain an influential role in shaping economic and political agendas that hinder development behind a guise of responsible and sustainable behaviour. A local-level analysis of Barrick Gold Corporation’s actions in Peru is connected to global economic and political trends to show how hegemony serves the maintenance of neoliberal economic growth instead of social development.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.022 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".