Corporate Masking: (In)visibility, Petro-nationalism, and Role Play in the Line 3 Pipeline Battle
Bibliographic record
Abstract
This article investigates the concept of corporate masking – its ability to reveal and conceal simultaneously and, within this duality, transform that which is masked – as a performance strategy used by energy companies during high-profile and highly visible frontline pipeline battles. Using Enbridge’s Line 3 Replacement project and the subsequent protests as a case study, I examine how the company attempted to transform how the public viewed and interacted with its political and social influence over the approval and construction of the pipeline. Drawing on discussions of petro-nationalism, visibility politics, and theatrical masking, this article explores how the company enacted a corporate masking performance to maintain control over its desired visibility and establish itself as a neutral arbiter of energy transportation and security. I examine how Enbridge’s masking performance had a distinctly embodied dimension that created what this article explores as embodied assignment, a form of capitalist subject formation in alignment with petro-nationalist desires, curated and colonized by energy companies. The paper aims to explore how companies engage in anti-democratic politics on the frontlines when their infrastructure and company image experience negative, heightened visibility.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".