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Record W2158293084 · doi:10.19030/iber.v13i2.8442

An Analysis Of The Tolerance For Ambiguity Among Accounting Students

2014· article· en· W2158293084 on OpenAlexaff
L.P. Steenkamp, P. L. WESSELS

Bibliographic record

VenueInternational Business & Economics Research Journal (IBER) · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAccounting Education and Careers
Canadian institutionsNiagara College
Fundersnot available
KeywordsAmbiguityAmbiguity toleranceFeelingAccountingNorm (philosophy)PsychologyPersonalityPopulationScale (ratio)Social psychologyCertaintyMedicinePolitical scienceComputer scienceBusinessMathematicsLawGeographyEnvironmental health

Abstract

fetched live from OpenAlex

Tolerance for ambiguity is a personality characteristic that reflects the general feelings and attitudes of an individual toward ambiguity and ambiguous situations. Prior research has found accounting students to be significantly less tolerant of ambiguity than the general population, with female accounting students less tolerant than their male counterparts. The research on which this article is reporting aimed to examine the personality characteristic tolerance for ambiguity among accounting students to determine whether those currently being attracted to the profession are still less tolerant of ambiguity than the norm as reported by prior research, taking into account the drive from the accounting profession to attract individuals to the profession who are effective communicators, who can think and act strategically, are able to solve unstructured problems, and are aware of business issues. Tolerance for ambiguity was measured using the AT-20 scale, a widely used and validated instrument developed by MacDonald (1970). The results of this study confirm that students enrolled for accounting degree programmes are less tolerant for ambiguity (mean = 7.50) than students enrolled for other business programmes (mean = 8.16). Female students enrolled in accounting degree programmes were significantly less tolerant for ambiguity than their male counterparts.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0020.000
Research integrity0.0000.000
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 head0.038
GPT teacher head0.355
Teacher spread0.317 · 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 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

Citations4
Published2014
Admission routes1
Has abstractyes

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