Bibliographic record
Abstract
Abstract Manuscript Type Perspective Research Question/Issue Can maximizing shareholder value maximize social value? Research Findings/Insights If good corporate governance is defined as maximizing a firm's contribution to overall social welfare, shareholder valuation maximization can achieve this only if capital markets are functionally efficient, a concept quite distinct from the definitions of market efficiency usually found in finance textbooks. Functional efficient capital markets allocate capital to its highest value uses subject to achieving tolerable success toward other social goals, such as equality or environmental standards. Theoretical/Academic Implications Pressing top managers to maximize shareholder valuation is of questionable social value if share prices are either informationally inefficient (noisy) or informationally efficient but functionally inefficient (share prices faithfully reflect fundamental values, which depend on political lobbying, gaming complex regulations, etc., more than genuine productivity growth). Practitioner/Policy Implications Shareholder valuation, if surrounded by institutions that foster functional efficiency, is a readily observable, legally useful, and socially defensible barometer of corporate governance. The efficacy of corporate governance institutions associated with shareholder value thus depends on the bundle of political economy institutions that promote functional efficiency.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".