What do we know about what is going on inside the boardroom?
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
Purpose The fundamental role of corporate boards is to monitor and advise top management on strategic issues. It is therefore of the utmost importance that corporate directors are effective as a decision-making group to ensure corporate performance (Zattoni et al., 2015; Minichilli et al., 2012). But, what do we know about what is really going on inside the boardroom? This study aims to shed light on this important question. Design/methodology/approach The authors undertake a targeted review of the literature to take account of all publications regarding board dynamics in relation to board effectiveness. Findings This study shows that we know very little about what is going on inside the “black box” of board dynamics and its relation to how effective directors are at doing their job, namely, monitoring and advising top management and establishing and expanding the firm’s network, to gain access to the resources it needs. The authors propose several avenues of research to better understand board dynamics. Originality/value In this study, the authors show how and why the present body of knowledge on team effectiveness should be harnessed to better understand corporate board dynamics in relation to board effectiveness.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.011 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.009 |
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