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Record W3027449695 · doi:10.1515/9781501716133-007

6. Learning from Food Laws in Nova Scotia

2019· book-chapter· en· W3027449695 on OpenAlexaboutno aff

Bibliographic record

VenueCornell University Press eBooks · 2019
Typebook-chapter
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsNova scotiaNova (rocket)LawPolitical scienceHistoryEngineeringEthnologyAeronautics

Abstract

fetched live from OpenAlex

Learning from Food Laws in Nova ScotiaOn the first Thursday of October, 1781, St. George Tucker, who would go on to write a few lines about hunger personified, reported almost four hundred dead horses floating in and sprawled along the shore of the York River in Virginia.The corpses indicated that Lord Charles Cornwallis had ordered the animals killed to save on forage and had "no Intention of pushing a march" from his besieged position at Yorktown.Cornwallis was close to surrendering to the American rebels.His men, who had been short on provisions even before reaching Yorktown, now battled an outbreak of smallpox. 1Throughout the Revolutionary War, ex-bondpeople had prevented British hunger.But when the tides of the war shifted, the military had little use for black victual warriors, for the provisioners like David George and Boston King who had supplied soldiers, or for their families.Formerly enslaved men, women, and children strug gled to obtain British help after Yorktown.Cornwallis ousted the runaways from a hospital at Gloucester to save on rations, while dogs ate the amputated limbs left behind.It was during this chaos that Boston King heard rumors that the British were planning to return escapees to former masters, and it was also during this period that he recalled his loss of appetite.Yet people like Boston King, and like David George, who had used provisioning roles to ensure their mobility from one colony to another, also managed to leave the former American colonies and go to Nova Scotia.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.988
Threshold uncertainty score1.000

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.043
GPT teacher head0.185
Teacher spread0.141 · 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.

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
Published2019
Admission routes1
Has abstractyes

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