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Record W4320519074 · doi:10.1017/9781782042648.001

Introduction

2014· other· en· W4320519074 on OpenAlexaboutno aff
Allen J. Frantzen

Bibliographic record

Venuenot available
Typeother
Languageen
FieldAgricultural and Biological Sciences
TopicCulinary Culture and Tourism
Canadian institutionsnot available
Fundersnot available
KeywordsButcherLiving spaceGeographyAgricultural scienceGenealogyHistorySociologyBiologyArchaeologyDemography

Abstract

fetched live from OpenAlex

The Anglo-Saxons understood material things in a way most people no longer do. Yet their relationship to objects is not entirely beyond our grasp. In the 1950s, when I was a boy, rural life in the United States bore some resemblance to conditions in Anglo-Saxon England. Animals, plants, and the tools needed to manage and process them filled our living space. Cows had to be milked and fed every day, including Thanksgiving, Christmas, and all the other holidays. Family feasts included meat from our own animals – pork sausage in the turkey stuffing, perhaps – and canned or frozen vegetables from the garden. We experienced the relationship between plants and animals and food on the table directly. Although there currently is a trend for restaurants and food businesses to emphasize local produce and stress the connection between what is on the plate and where it was grown, few people butcher their own animals or grow their own food any longer. In my youth this was not the case. Until the period after World War I, most people in the United States, Canada, and Great Britain lived in the countryside, like my family. The shift from rural to urban was gradual. The US Census Bureau shows that the split was 60 percent to 40 percent in favor of the rural population in 1900. By 1920 the distribution was almost even, 51 percent rural to 49 percent urban. Thereafter the shift was more pronounced: 44 percent to 56 percent in 1940; 30 percent to 70 percent in 1960. By 1990 the United States was 25 percent rural and 75 percent urban. Much of this change, in which over one-third of the country’s population was reclassified as urban, happened in the first two decades of the century (when the rural population shrank by nine percentage points) and between 1940 and 1960 (when the rural population declined a further fourteen points). Losses since have been much smaller, in part because rural life itself has changed. Blue collar workers who live in the countryside drive to factory jobs and shop in supermarkets and discount chains. Their habits are more urban than rural. Cities have changed too. Until recently it was common for people in some urban areas to raise their own animals and to have large gardens.

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)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.513
Threshold uncertainty score0.732

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0030.001
Scholarly communication0.0070.005
Open science0.0030.005
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.4870.312

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 head0.009
GPT teacher head0.188
Teacher spread0.179 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

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
Published2014
Admission routes1
Has abstractyes

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