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Record W2475995793 · doi:10.1057/9781137292926_1

Realms of Gold

2013· book-chapter· en· W2475995793 on OpenAlexaff
Alexander Dick

Bibliographic record

VenuePalgrave Macmillan UK eBooks · 2013
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicHistorical Economic and Social Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPledgeNegotiable instrumentBullionBarterMoney marketCommerceLawEconomyHistoryBusinessEconomicsPolitical scienceFinanceMarket economy

Abstract

fetched live from OpenAlex

In June 1816, the British Parliament did something no one had ever done before: it officially introduced a gold standard. Until 1816, ‘money’ in Britain comprised a massively heterogeneous network of varying exchange practices and social conventions. It existed in a variety of forms: bullion, coins, tokens, bills of exchange, promissory notes, government bills, and banknotes. These were exchangeable in a number of different institutions: goldsmiths, shops, country and metropolitan banks, factories, and agencies. Forgery was a crime and a nuisance, but for many people it was also a way to facilitate commerce where sanctioned practices were inaccessible. Money also had social meanings. In early-modern times, money was conceived metaphorically as blood, food, animals, and birds. Money was life. It could also be death: ill-got riches were wounds; excessive debt was a disease. Francis Bacon once remarked that ‘money is dirt’ in both senses of the term, land and muck. Money was a means for people to calculate their social worth. It was a way to distinguish the rich from the poor, men and women, husbands and wives. Money could be given as a gift, received as a token of gratitude, held up as a pledge of honor, and hoarded as a form of rebellion. The ability to create money was a sign of ingenuity; destroying it meant power. Money was a means of escape and a mark of bondage, a charm and a curse, a ticket to ride and a prison sentence. 2 These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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)0.004
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: Other
Teacher disagreement score0.032
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0140.022
Scholarly communication0.0110.008
Open science0.0010.010
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0320.009

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 head0.036
GPT teacher head0.204
Teacher spread0.168 · 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
Published2013
Admission routes1
Has abstractyes

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