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Record W2554004454 · doi:10.1002/sea2.12043

Inside the halo zone: Geology, finance, and the corporate performance of profit in a deep tight oil formation

2016· article· en· W2554004454 on OpenAlexaffabout
Caura Wood

Bibliographic record

VenueEconomic Anthropology · 2016
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsYork University
Fundersnot available
KeywordsMateriality (auditing)CorporationShareholder valueEconomicsPetroleum industryPerformativityCorporate governanceShareholderFinanceBusinessSociologyGeology

Abstract

fetched live from OpenAlex

This article explores the entanglement of petroleum geology and finance with the identification and qualification of tight oil prospects in Alberta. Tracing the corporate history of an entrepreneurial energy corporation on the verge of liquidation, the article explores the calculative agencies and forms of subsurface qualification that matter to making or losing money. Specifically, it illustrates how a financialized geology navigates the sedimented spaces of industry, mapping the very pore spaces of subterranean rock by using big data and other prosthetic devices, such as well logs, in the pursuit of shareholder value. Drawing from science and technology studies perspectives on the performativity of financial models, and connecting those to the emerging anthropological interest in “resource materiality,” (Richardson et al. 2014) this article ethnographically foregrounds how finance shapes discoveries and formats them into the framework of finance, particularly the formulas of “net present value” and “discounted future cash flows,” which are constitutive of shareholder value, corporate profit, and hydrocarbon futures. The article argues that more anthropological attention needs to be given to how these formulas and their fictions work to materialize a financial path dependency on future hydrocarbon liquidation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.746
Threshold uncertainty score0.326

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.188
Teacher spread0.180 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations22
Published2016
Admission routes2
Has abstractyes

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