Energy Sovereignty and Corporate Social Responsibility
Bibliographic record
Abstract
This paper will examine how corporate social responsibility (CSR), specifically related to the challenges of climate change, is integrated in oil and gas business models using a stakeholder theory approach. The paper will draw upon a case study of the Canadian oil and gas industry, looking at multinational corporations’ institutional pressures with respect to stakeholders, and challenges to their legitimacy, in Canada in comparison to MNC oil and gas operations elsewhere. The Arctic environmental region is home to Canada’s most significant reserves of hydrocarbons, oil and gas, but changes which are being exacerbated by shifts in the earth’s climate will ultimately make the environmental planning process more challenging for companies looking to expand their interests in the Arctic and for the sovereignty debates over land claims and land use. This is not only true because of the changes in the environment itself, but because of the effects of these changes on First Nations communities. This paper will show that long-term changes in environmental frameworks are one of the reasons why cumulative and collaborative CSR efforts are warranted in order to ensure that there is a balance between the interests of different parties. This will be achieved through a project development framework linked to a CSR approach grounded in stakeholder stewardship, rather than self-interest, that recognizes multiple levels of sovereignty in the control and use of resources.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.026 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".