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Record W6980854417

Custom cutters: A history of custom combining on the Great Plains

2016· dissertation· en· W6980854417 on OpenAlexaboutno aff

Bibliographic record

VenueSHAREOK (University of Oklahoma) · 2016
Typedissertation
Languageen
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsnot available
Fundersnot available
KeywordsWork (physics)Plan (archaeology)Feature (linguistics)Process (computing)Field (mathematics)
DOInot available

Abstract

fetched live from OpenAlex

To farmers on the Great Plains custom combining is an accepted institution, one so taken for granted that its origins are obscure.This study refreshes the memory of those beginnings and traces the development of the business to the present.The subjects of the study are known variously as "custom combiners," "custom harvesters," "contract harvesters," and "wheaties," but most often as "custom cutters."Owners of combines may do custom work locally, but I focus here on itinerant custom cutters who travel north with the harvest.The geographic scope is the Great Plains of the United States, with occasional attention to parallel developments in Canada.My perspective is frankly environmental, interpreting custom combining as one of the many peculiar adaptations that characterize life on the Great Plains.Sou...""'Ces for writing the history of custom combining are massive and yet fragmentary.I have made certain decisions as to documentation that should be explained.. Sometimes generalizations in the ~ext were based on too many sources to list, and so I have cited only the most important sources.This was necessary because there were few secondary sources to rely on, In other cases the text may run for pages without a footnote, Many of my statements about the operations and lives of custom cutters stem from personal observation of work in the field and informal conversations with harvesters in such places as elevators or iii cafes, contacts too infomal to be temed "interviews," To footnote personaJ.observation seemed pompous.This work should be regarded as broad and exploratory.Economists, geographers, and sociologists someday may launch more specialized and structured investigations of custom combining.I have carried the inquiry far enough to draw some significant generaJ.izations.Thanks beyond words are due to Dr. Norbert Mahnken, director of this dissertation, whose guidance and help made difficult situations manageable, Dr.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.044
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.005
Science and technology studies0.0080.010
Scholarly communication0.0030.003
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0140.001

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 head0.033
GPT teacher head0.242
Teacher spread0.208 · 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 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
Published2016
Admission routes1
Has abstractyes

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