Board Composition and Accounting Conservatism: The Role of Business Experts, Support Specialist and Community Influentials
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.
Bibliographic record
Abstract
Abstract In this study, we examine the relationship between accounting conservatism and board composition. We categorise outside directors according to their skills, abilities, connections and knowledge in three different categories: business experts, support specialists and community influentials. We address three main questions: Is the financial and accounting expertise of directors relevant to improving accounting conservatism? Does specialised expertise in the board affect the speed at which news is reflected in earnings? And how do the political ties of directors affect the sensitivity of earnings to bad news? Our sample consists of active US biotech firms publicly traded on the NYSE, AMEX and NASDAQ stock exchanges during the 2005–2013 period. Our study confirms that not all outside directors are equally effective in monitoring and contracting and that certain kinds of outside directors, such as politicians, can even lower the sensitivity of earnings to bad news. Our robustness analysis confirms that these results are not conditional on the accounting measure, and suggest that distinguishing directors according to their skills and abilities is crucial to understanding the way in which firm boards affect conservatism.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it