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Record W4381736118 · doi:10.1080/09638180.2023.2223590

Does Board Independence Influence Annual Report Readability?

2023· article· en· W4381736118 on OpenAlex
Dewan Rahman, Muhammad Kabir

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

VenueEuropean Accounting Review · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsReadabilityIndependence (probability theory)AccountingSample (material)Association (psychology)BusinessEditorial boardPsychologyStatisticsComputer scienceMathematicsLibrary science

Abstract

fetched live from OpenAlex

We examine whether board independence improves the readability of annual reports. Using a sample of 11,938 firm-year observations over the period 1997–2016, we empirically show that board independence decreases the readability of annual reports. This result is consistent with the notion of managerial avoidance of costly board monitoring. We also run an array of cross-sectional tests to understand the settings in which the association is either more or less pronounced. Even though managerial ability and SEC’s Plain English Rule of 1998 improve readability, we find that the negative relationship between board independence and readability continues to persist. Our results also reveal that directors’ tenure and CEO duality attenuate the board independence–readability relationship, while CEO tenure enhances this association. Overall, our findings provide additional insights into the link between board independence and annual report readability.

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)0.006
metaresearch head score (Gemma)0.039
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.578
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.039
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0010.003
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.015

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 head0.010
GPT teacher head0.239
Teacher spread0.229 · 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