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Record W2793325026 · doi:10.1111/abac.12123

R. J. Chambers on <i>Securities and Obscurities</i>: Making a Case for the Reform of the Law of Company Accounts in the 1970s

2018· article· en· W2793325026 on OpenAlexaff
Martin Persson, Christopher J. Napier

Bibliographic record

VenueAbacus · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAccounting and Organizational Management
Canadian institutionsWestern University
Fundersnot available
KeywordsRigourNarrativeInflation (cosmology)Perspective (graphical)AccountingEconomicsLawEmpirical researchLaw and economicsPositive economicsSociologyPolitical scienceMacroeconomicsEpistemologyPhilosophyLinguistics

Abstract

fetched live from OpenAlex

This study examines the contribution of Raymond J. Chambers to the British inflation accounting debate in the early‐to‐mid 1970s, from the perspective of the reception of his book, Securities and Obscurities: A Case for Reform of the Law of Company Accounts, published in 1973. To structure the empirical narrative, drawing on previously unpublished documents from the R. J. Chambers Archives, we employ Czarniawska and Joerges’ ( ) notion of the ‘travel of ideas’, and Mumford’s ( ) observation of the existence of ‘inflation accounting debate cycles’. The result is a narrative that traces the environmental and material circumstances that led to Chambers’ book having a lesser impact on the British inflation debate than one would expect based on the international exposure of his ideas, his influence at the time, and the empirical rigour of his proposal. The purpose of this exercise is to assess how contextual factors, such as the choice of publisher, use of promotional material, and distribution methods, can be as (or more) important than the substance of the proposed ideas, arguments, and solutions.

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)0.003
metaresearch head score (Gemma)0.010
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 score0.069
Threshold uncertainty score0.138

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0060.013
Scholarly communication0.0100.006
Open science0.0010.002
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0040.001

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 head0.021
GPT teacher head0.241
Teacher spread0.220 · 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

Citations10
Published2018
Admission routes1
Has abstractyes

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