MétaCan
Menu
Back to cohort
Record W4396694561 · doi:10.3828/whp.eh.63830915903590

Ignorance and Environmental History: The Opening of an Arctic Offshore Oil Frontier, 1968–1976

2024· article· en· W4396694561 on OpenAlexaboutno aff
Andrew Stuhl

Bibliographic record

VenueEnvironment and History · 2024
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsFrontierIgnoranceSubmarine pipelineArcticThe arcticPetroleumEnvironmental scienceOceanographyGeographyGeologyPolitical scienceArchaeologyLawPaleontology

Abstract

fetched live from OpenAlex

Ignorance holds untapped explanatory power for environmental history, in the Arctic and beyond. I define ignorance as a state of limited knowledge, held by groups, and produced through social processes. The case study is the opening of an oil frontier in the Canadian Beaufort Sea between 1968 and 1976. Drawing from a growing body of scholarship on ignorance, as well as newly available governmental and oil industry records, I review three concepts environmental historians can use to analyse the production of ignorance. These concepts are: proprietary knowledge, selective transmission and undone research. Taken together, these concepts make visible a set of political, economic and environmental conditions that allowed ignorance to shape the approval of the first offshore drilling programme in the Canadian Beaufort Sea. Ultimately, this case study demonstrates that ignorance – like its cousins doubt or uncertainty – has been a resource that extractive industries and governmental regulators have manipulated to navigate evolving requirements of environmental planning.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.936
Threshold uncertainty score0.520

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0230.046
Scholarly communication0.0040.004
Open science0.0010.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.024
GPT teacher head0.255
Teacher spread0.231 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations1
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueEnvironment and HistorySame topicIndigenous Studies and EcologyFrench-language works237,207