Bibliographic record
Abstract
In Chapter 1, we analyse a model in which a biased Institutional Investor may offer a share price to acquire voting rights over a corporate motion. The selling and voting decisions of each initial shareholder (with imperfect, private information about the firm’s value of the motion) depend on the private information held, but the selling decision also depends on the Institutional Investor’s price offer. The voting decision of the Institutional Investor depends on the bias and updated beliefs about the motion value (which depend on the quantity of acquired shares). We find that (i) if the magnitude of the bias is large enough, then, relative to the absence of the Institutional Investor, shareholder welfare increases (decreases) if the bias is against (for) the motion and (ii) the Institutional Investor is less willing to exert control as the quality of the private information increases. In Chapter 2, we use the ex-vote date of shareholder meetings to measure the value of corporate shareholder voting rights. We find that the value of voting rights is affected by the quantity and type of shareholder proposed motions and that the interaction between shareholder and management proposed motions negatively affects the value of voting. Our results suggest that the mean value of voting rights is about 1.01% of firm value. The measured drop in the ex-ante expected share price on the ex-vote date is analogous to the drop in share value after a price offer in the modelin Chapter 1. In Chapter 3, we analyse an intertemporal model of network formation. Individuals possess a quality variable whose evolution depends on the number and quality of the individual’s links. We introduce a definition of positive assortative networks and provide conditions for which our model of network formation supports positive assortative networks.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".