Inside the halo zone: Geology, finance, and the corporate performance of profit in a deep tight oil formation
Bibliographic record
Abstract
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".