MétaCan
Menu
Back to cohort
Record W4388779896 · doi:10.1515/9781552384909

Somebody Else's Money

2012· book· en· W4388779896 on OpenAlexaboutno aff
Warren M. Elofson

Bibliographic record

VenueUniversity of Calgary Press eBooks · 2012
Typebook
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsEconomics

Abstract

fetched live from OpenAlex

The Walrond Ranch, a cattle and horse operation in the foothills of southern Alberta, was one of the four giants of the livestock grazing industry in the late nineteenth and early twentieth century. At its height, the Walrond ran over 10,000 cattle along with several hundred well-bred Clydesdale and Shire horses on nearly 300,000 acres of land. Ultimately, however, the Walrond failed. The driving force behind the ranch, Dr. Duncan McNab McEachran, had high aspirations and communicated his optimism to Sir John Walrond and the rest of the British investors funding the venture. But reality quickly set in. Winter storms, drought, disease, and predators constantly depleted the Walrond's herds and the operation inexorably slipped toward bankruptcy. McEachran's poor management played just as large a role as the environmental challenges in the ranch's downfall; his stubborn reluctance to admit failure prolonged the inevitable, wasting more and more investor dollars in the meantime. Somebody Else's Money: The Walrond Ranch Story, 1883 1907, is the first close environmental and economic study of one of the so-called "great" ranches on the northern Great Plains of North America. Warren Elofson examines the business side of large-scale, open range grazing and describes the myriad of natural and man-made obstacles that barred it from success. He argues that, financially, the Walrond was doomed from the beginning because its management approach and grazing practices were unsuited to both the natural and economic conditions of the frontier environment.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.759
Threshold uncertainty score0.996

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 studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.193
Teacher spread0.176 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueUniversity of Calgary Press eBooksSame topicCanadian Identity and HistoryFrench-language works237,207