Statoil in the Canadian Oil Sands: Tar versus Oil and the trouble of storytelling
Bibliographic record
Abstract
While petroleum production and industry actors have received much attention when it comes to environmental issues, market alterations and geopolitics, less heed has been directed towards the specific accounts of reality such actors simultaneously produce. This study shows that to make oil “work” in today’s society is about more than physical extraction, transportation and distribution: Also worldviews and arguments for oil production activities and oil as reasonable product are imperative. It is about properly ‘configuring’ the setting in which the product must enter. \nThe research project has followed the Norwegian state-owned oil producer Statoil (today Equinor), in their decade-long involvement in the Canadian oil sands, from 2006 to 2016. To move on land posed some challenges as well as opportunities for a company that mainly specializes in offshore production. Approximately 70 per cent of the discovered oil resources in the world are of heavy oil quality, with the Canadian oil sands in Alberta as biggest known site. This fact alone made the oil sands an attractive business case, initially. However, Statoil, internationally credited as a “clean and green” producer, then also became part of the most land-seizing, energy demanding and emission-intensive oil production the world has seen – often referred to as “the Mordor of oil production”. As such, Statoil entered quite an unfamiliar, controversial ‘site’, which had certain effects for the company and the public debates at home, and in Canada. Taking Statoil’s specific experiences with engaging in the production of Canadian oil sands seriously, this study reveals how they had to balance many different concerns when doing so. How did Statoil strive to make room for the oil sands as an acceptable solution in their portfolio, in a world increasingly aware of climate concerns? What were the initial arguments for entering, and how did Statoil communicate their involvement? \nBased in the interdisciplinary research field Science and technology studies (STS), this study seeks to push the field further, by having an explicit methodological ambition of showing how stories and storytelling can be studied in new, specific ways. The study employs well-known concepts from STS, and combines this with resources derived from narrative theory, to make a novel approach focusing on how narratives are used, and produced, by prominent societal actors. Investigating both the content of Statoil’s oil sands stories, and the circumstances that prompted their storytelling, this study demonstrates how content and context is coproduced within the stories made. Simultaneously asking ‘how have Statoil’s activities and stories about own project been met and protested to, in certain settings’; other actors also enter and perform in the material and analyses. In this way, the study shows how opposition towards Statoil not only comes from NGOs and other obvious antagonists, but also from within the Canadian oil sands industry. \nBy going in-depth on a handful of empirical episodes and situations Statoil were part of in the years they operated in Alberta, the study sheds light on the conflicting narratives made about the oil sands, and the crucial role different production technologies play in this.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.038 | 0.033 |
| Scholarly communication | 0.022 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".