Bibliographic record
Abstract
Commentators have argued that employees should be compensated in the event of a hostile takeover; otherwise, the threat of such a takeover will fail to incentivize firm-specific investments by employees.Such deferred compensation is analogous to the payment of damages following a breach of contract.The analogous breach, here, is the breach of an implicit contract between management and employees.Employees trusted management to compensate them for firm-specific investments not explicitly contracted for.I use a familiar result from the contract law literature: There is no measure of damages for breach of contract that can generate both efficient breach and efficient investment by parties to the relationship.While zero damages results in an inefficiently high likelihood of breach, expectation damages result in too much investment.Similarly, in the hostile takeover context, no measure of ex post compensation to employees can generate efficient takeovers from outside bidders and efficient firmspecific investment by employees.Measures of compensation that incentivize only those takeovers that are efficient will lead to overreliance, i.e., excessive firm-specific investments.Essentially, trying to plug one leak exposes another.
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 | 0.023 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.013 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".