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Record W2154472149 · doi:10.1506/7kbw-bkcu-ttar-164l

A Note on the Interdependence between Hypothesis Generation and Information Search in Conducting Analytical Procedures*

2003· article· en· W2154472149 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueContemporary Accounting Research · 2003
Typearticle
Languageen
FieldDecision Sciences
TopicForecasting Techniques and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsSet (abstract data type)AuditStatistical hypothesis testingQuality (philosophy)Process (computing)Alternative hypothesisPsychologyComputer scienceEconometricsStatisticsMathematicsAccountingEconomicsNull hypothesisEpistemology

Abstract

fetched live from OpenAlex

Abstract This study examines the linkage among the initial hypothesis set, the information search, and decision performance in performing analytical procedures. We manipulated the quality of the initial hypothesis set and the quality of the information search to investigate the extent to which deficiencies (or benefits) in either process can be remedied (or negated) by the other phase. The hypothesis set manipulation entailed inheriting a correct hypothesis set, inheriting an incorrect hypothesis set, or generating a hypothesis set. The information search was manipulated by providing a balanced evidence set to auditors (i.e., evidence on a range of likely causes including the actual cause ‐ analogous to a standard audit program) or asking them to conduct their own search. One hundred and two auditors participated in the study. The results show that auditors who inherit a correct hypothesis set and receive balanced evidence performed better than those who inherit a correct hypothesis set and did their own search, as well as those who inherited an incorrect hypothesis set and were provided a balanced evidence set. The former performance difference arose because auditors who conducted their own search were found to do repeated testing of non‐errors and truncated their search. This suggests that having a correct hypothesis set does not ensure that a balanced testing strategy is employed, which, in turn, diminishes part of the presumed benefits of a correct hypothesis set. The latter performance difference was attributable to auditors' failure to generate new hypotheses when they received evidence about a hypothesis that was not in the current hypothesis set. This demonstrates that balanced evidence does not fully compensate for having an initial incorrect hypothesis set. These findings suggest the need for firm training and/or decision aids to facilitate both a balanced information search and an iterative hypothesis generation process.

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.

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)0.022
metaresearch head score (Gemma)0.032
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.315
Threshold uncertainty score0.976

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0220.032
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
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 head0.529
GPT teacher head0.476
Teacher spread0.053 · 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