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Record W2890175958 · doi:10.1111/ehr.12783

An efficient market? Going public in London, 1891–1911

2018· article· en· W2890175958 on OpenAlexfundno aff
Sturla Lyngnes Fjesme, Neal Galpin, Lyndon Moore

Bibliographic record

VenueThe Economic History Review · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHistorical Economic and Social Studies
Canadian institutionsnot available
FundersUniversiteit van TilburgQueen's UniversityEconomic History SocietyBond UniversityRheinische Friedrich-Wilhelms-Universität BonnUniversity of MelbourneUniversitetet i Stavanger
KeywordsInitial public offeringStock exchangeBusinessPreferenceStock (firearms)Quality (philosophy)Capital marketCapital (architecture)Monetary economicsFinanceMarket economyEconomics

Abstract

fetched live from OpenAlex

Abstract There have been claims that British capital was not well deployed in Victorian Britain. There was, allegedly, a lack of support for new and dynamic companies in comparison to the situation in Germany and the US. We find no evidence to support these claims. The London Stock Exchange welcomed young, old, domestic, and foreign firms. It provided funds to firms in old, existing industries as well as patenting firms in ‘new‐tech’ industries at similar costs of capital. If investors did show a preference for older and foreign firms, it was because those firms offered investors better long‐run performance. In addition, we show some evidence that investors who worked in the same industry and lived close to the firm going public were allotted more shares in high‐quality initial public offerings.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.091
Threshold uncertainty score0.181

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0020.004
Scholarly communication0.0050.004
Open science0.0010.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0180.002

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 head0.061
GPT teacher head0.237
Teacher spread0.176 · 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 designObservational
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

Citations11
Published2018
Admission routes1
Has abstractyes

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