Social Media Portrayals of Three Extractives Companies’ Funding of Sport for Development in Indigenous Communities in Canada and Australia
Bibliographic record
Abstract
The extractives industry (mining, oil, and gas) engages in corporate social responsibility (CSR) activities to reinforce its organizational legitimacy and enhance its public image. One such approach to CSR that is popular in the industry is through funding sport initiatives aimed at Indigenous peoples (often termed Sport for Development; SFD). On the surface, such funding may seem commendable and innocuous; however, questions have been raised about the ways in which such funding may obfuscate the harmful impacts that the extractives industry has had and continues to have on Indigenous peoples and their traditional territories. Through the adoption of a postcolonial theoretical perspective and in conjunction with netnographic methods and discourse analysis, this project involved a consideration of how extractives companies portray their funding of sport programs in Indigenous communities on social media. Given the research focus on Indigenous communities in the countries known as Canada and Australia, between country differences were also examined. Three discourses related to the extractives industry’s funding of SFD in Indigenous communities in Canada and Australia were developed. These discourses included the following: 1) Extractives companies are proud “partners” of Indigenous communities; 2) Extractives companies are committed to helping Indigenous communities in Canada and Australia; and 3) Canadian extractives companies are future focused and past-blind, while Australian extractives companies are advocates for reconciliation. Overall, extractives companies in Canada and Australia were found to use social media to portray themselves as responsible and committed partners of Indigenous communities, while obscuring the ongoing histories of colonialism through discourses of empowerment and development through sport. Suggestions are made regarding ongoing interrogation of the ways in which the extractives industry perpetuates colonialism.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".