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Record W4387558295 · doi:10.1017/9781009392129.010

Conclusion

2023· book-chapter· en· W4387558295 on OpenAlexaff

Bibliographic record

VenueCambridge University Press eBooks · 2023
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicHistorical Economic and Social Studies
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsHypocrisyFeelingCode (set theory)Power (physics)Corporate governanceCharacter (mathematics)Law enforcementLawHistoryPolitical scienceSociologyAestheticsPsychologySocial psychologyComputer scienceManagementArtEconomics

Abstract

fetched live from OpenAlex

The transition from one culture of governance to another explains the character and timing of changes in the nature, location and scale of English executions from 1660 to 1900. Traditional landed elites adhered both to a “Bloody Code,” whose enforcement against common criminals could be regularly adjusted through consultations between trial judges and themselves, and to the occasional use of prolongedly agonizing execution rituals against traitors. The men who dominated England’s uniquely extensive and steadily expanding urban realms, and embraced new cultures of desacralization, feeling and reason, increasingly viewed the purposes, numerical extent and staging of executions differently. As the numbers and power of urbane people grew, first the extent and finally the practices of execution were adjusted accordingly. The many paradoxes of “feeling”, however, ensured their continued commitment to execution for murder, and some measure of hypocrisy in their views of executions and the people who attended them.

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)0.006
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.197
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0060.003
Open science0.0010.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.1970.057

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 head0.051
GPT teacher head0.185
Teacher spread0.133 · 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
Published2023
Admission routes1
Has abstractyes

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