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Record W2799493994 · doi:10.1093/notesj/gjy027

Cost Book Companies: A Note on Cost and Profit Shares

2018· article· en· W2799493994 on OpenAlexaff
John Parkinson

Bibliographic record

VenueNotes and Queries · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Theory and Policy
Canadian institutionsYork University
Fundersnot available
KeywordsShareholderCashJoint-stock companyLimited liabilityStock (firearms)BusinessEconomicsFinanceCommerceHistoryCorporate governance

Abstract

fetched live from OpenAlex

CORNISH tin mines used a unique form of organizational structure called the cost book company. There are no surviving records of cost book accounts prior to the eighteenth century, but it is believed that they pre-date the Norman Conquest. They survived until the turn of the twentieth century. During the late nineteenth century their role was replaced by the limited liability joint stock company. One of the features of the cost book company was that there were periodic reckonings, at regular intervals of four months or fewer, and the balance of net costs or net receipts would be divided between the shareholders in proportion to their shareholdings. There would either be a call on the shareholders or a distribution of the cash surplus. The commonest numbers of shares in cost book companies were in repeated divisions of the whole by two: that is half, a quarter, an eighth, a sixteenth, a thirty-second, and so on. This contrasts with the way that joint stock companies tend to issue shares in round numbers such as 100, 1,000, 1 million, etc. We would like to suggest that there were practical reasons for cost book companies’ choice of shareholding values.

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)0.012
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: Empirical · Consensus signal: none
Teacher disagreement score0.079
Threshold uncertainty score0.157

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0030.004
Scholarly communication0.0060.010
Open science0.0010.001
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0130.002

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 head0.058
GPT teacher head0.274
Teacher spread0.216 · 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
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

Citations0
Published2018
Admission routes1
Has abstractyes

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