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Record W2515459346 · doi:10.1111/jbfa.12220

The Influence of Country‐ and Firm‐level Governance on Financial Reporting Quality: Revisiting the Evidence

2016· article· en· W2515459346 on OpenAlex
Pietro Bonetti, Michel Magnan, Antonio Parbonetti

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Business Finance &amp Accounting · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsConcordia University
Fundersnot available
KeywordsCorporate governanceEnforcementBusinessAccountingDiscretionQuality (philosophy)Sample (material)FinanceInternational Financial Reporting StandardsPolitical science

Abstract

fetched live from OpenAlex

Abstract This paper examines how firm‐level governance and country‐level governance interplay in shaping financial reporting quality. Using IFRS adoption as a source of variation in firms’ reporting discretion, and a large sample of European firms that mandatorily switch to the new set of standards, we find that in countries with low enforcement and weak oversight over financial reporting, only firms with strong board‐level corporate governance mechanisms experience an increase in financial reporting quality, consistent with firm‐ and country‐level governance mechanisms being substitutes. However, in countries with high enforcement and strict oversight over financial reporting, firms with either strong or weak board‐level governance mechanisms experience an increase in financial reporting quality, even if the increase is larger for the former group. Overall, our findings indicate that in the debate about the effects of governance on the quality of financial reporting, it is important to consider both country‐ and firm‐level corporate governance mechanisms.

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)0.009
metaresearch head score (Gemma)0.214
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 score0.583
Threshold uncertainty score0.943

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.214
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.004
Open science0.0010.001
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 head0.035
GPT teacher head0.270
Teacher spread0.235 · 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