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Accounting Theory Lenses to Analyze Literature: An Exploration through Shakespeare’s Macbeth

2018· article· en· W2905099491 on OpenAlexaff
Patrice Gélinas, Camilia Gélinas, Lisa Baillargeon

Bibliographic record

VenueInternational Journal of Applied Linguistics & English Literature · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsUniversité du Québec à MontréalMcGill UniversityYork University
Fundersnot available
KeywordsAbstractionSample (material)Process (computing)EpistemologyExploratory researchAccountingSociologyLiteraturePsychologyComputer sciencePhilosophyArtEconomicsSocial sciencePhysics

Abstract

fetched live from OpenAlex

The present study is the first of its kind to suggest that accounting theory lenses can be a powerful tool to understand literary works, such as a diegesis. Using Shakespeare’s Macbeth as exploratory setting, we propose a metaphorical abstraction process to translate and analyze characters and currencies. We then illustrate the effectiveness of an accounting theoretical framework to analyze a sample of play events which allude to its finality. The main contribution of this paper is to illustrate how the work of literature and accounting theorists can be mutually illuminating.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.025
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.914
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0030.002
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.257
Teacher spread0.246 · 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 teacher head, not a consensus.

Study designTheoretical or conceptual
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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