Extractives Companies’ Social Media Portrayals of Their Funding of Sport for Development in Indigenous Communities in Canada and Australia
Bibliographic record
Abstract
The extractives industry (mining, quarrying, 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 improving the lives of Indigenous peoples, known as sport for development (SFD). Through the adoption of a settler colonial studies lens, and using netnographic methods and discourse analysis, we examined how three extractives companies portray their funding of SFD in Indigenous communities in Canada and Australia on social media, and the ways in which it contributes to settler colonialism. We determined that there are two main discourses that extractive companies use: i) Extractives companies “help” and “partner” with Indigenous communities to enable Indigenous youth’s access to the transformative power of sport; ii) longevity is strategically associated with such “help” and “partnership.” The production of these discourses enables extractives companies to downplay their contributions to settler colonialism through land denigration and colonial authority.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".