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Enregistrement W7006007986

Statoil in the Canadian Oil Sands: Tar versus Oil and the trouble of storytelling

2024· dissertation· en· W7006007986 sur OpenAlexaboutno aff

Notice bibliographique

RevueDuo Research Archive (University of Oslo) · 2024
Typedissertation
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueReproductive biology and impacts on aquatic species
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésOil sandsPetroleumPetroleum industryProduct (mathematics)Oil refineryProduction (economics)Government (linguistics)Unconventional oilOil reserves
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,011
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: Qualitatif
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,144
Score d'incertitude au seuil0,993

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0040,011
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0030,005
Études des sciences et des technologies0,0380,033
Communication savante0,0220,009
Science ouverte0,0030,006
Intégrité de la recherche0,0040,005
Charge utile insuffisante (le modèle a refusé de juger)0,0130,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,030
Tête enseignante GPT0,288
Écart entre enseignants0,258 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeQualitatif
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2024
Routes d'admission1
Résumé présentoui

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