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Record W2343347744 · doi:10.1080/00076791.2016.1173031

The problem of milk in the nineteenth-century Ontario cheese industry: an envirotechnical approach to business history

2016· article· en· W2343347744 on OpenAlexaffabout
Hayley Goodchild

Bibliographic record

VenueBusiness History · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture, Land Use, Rural Development
Canadian institutionsMcMaster University
Fundersnot available
KeywordsScale (ratio)Dairy industryProduction (economics)Relevance (law)BusinessFood spoilageEconomyMarketingEconomicsPolitical scienceGeographyFood scienceLawBiology

Abstract

fetched live from OpenAlex

This article analyses Ontario’s export-oriented cheese industry and its challenges in the second half of the nineteenth century using an ‘envirotechnical’ approach. The reorganisation of cheese production from farms to rural factories in the 1860s increased opportunities for spoilage and adulteration of milk at the same time that it made detecting and managing the same more difficult, which compelled the provincial dairymen’s associations to develop quasi-managerial roles to contend with these unanticipated challenges. The ‘problem of milk’ highlights the extent to which the rural cheese industry was an ecological and envirotechnical process rather than an entity separate from the non-human world. Ultimately this case study offers one model for combining environmental and business histories at a scale beyond the individual firm while also highlighting the relevance of the local in the development of the global food system in the late-nineteenth century.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.131
Threshold uncertainty score0.484

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0110.021
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.028
GPT teacher head0.179
Teacher spread0.151 · 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
Published2016
Admission routes2
Has abstractyes

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