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Record W3200938094 · doi:10.31356/dpb014

Sixty Years of Boom and Bust: The Impact of Oil in North Dakota, 1958-2018

2020· book· en· W3200938094 on OpenAlexfundaboutno aff

Bibliographic record

VenueThe Digital Press at the University of North Dakota eBooks · 2020
Typebook
Languageen
FieldEconomics, Econometrics and Finance
TopicSocioeconomics of Resources and Conservation
Canadian institutionsnot available
FundersUniversity of Ottawa
KeywordsBoomBustPoliticsOil boomState (computer science)Natural resourceHistoryPolitical scienceEconomic historyEconomyGeographyEconomicsLawGeologyOceanography

Abstract

fetched live from OpenAlex

In the 1950s, North Dakota experienced its first oil boom in the Williston Basin, on the western side of the state. The region experienced unprecedented social and economic changes, which were carefully documented in a 1958 report by four researchers at the University of North Dakota. Since then, western North Dakota has undergone two more booms, the most recent from 2008 to 2014. Sixty Years of Boom and Bust republishes the 1958 report and updates its analysis by describing the impact of the latest boom on the region’s physical geography, politics, economics, and social structure. Sixty Years of Boom and Bust addresses topics as relevant today as they were in 1958: the natural and built environment, politics and policy, crime, intergroup relations, and access to housing and medical services. In addition to making hard-to-find material readily available, it examines an area shaped by resource booms and busts over the course of six decades. As a result, it provides unprecedented insight into the patterns of develop- ment and the roots of the challenges the region has faced. Kyle Conway is an associate professor of communication at the University of Ottawa.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.371
Threshold uncertainty score0.738

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0130.002

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 head0.172
Teacher spread0.148 · 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 designObservational
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

Citations5
Published2020
Admission routes2
Has abstractyes

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