Bibliographic record
Abstract
This paper proposes a model to analyze the dynamic relations between incentive contracts and analysts' effort in providing accurate research when both ethical and reputational concerns matter. First, we show that reputation picks up ability and thus serves as a sorting device: when analysts have a relatively low reputation for providing research quality (below a threshold level) banks find it more profitable to offer a mix of monetary and non monetary (ethic based) incentives and rely on the analyst's work ethic in ordre to provide research quality. Alternatively, when analysts have a high reputation, full financial (performance based) incentives contracts offer a substantial reward for their contribution to the firm's profits. Second, we find that the design of compensation contracts, in the presence of reputational concerns and work ethic, may lead to incentive problems: full financial incentives contracts may exacerbate conflicts of interest by giving analysts extrinsic rewards on reporting, thereby inducing them to prefer high short term benefits to the detriment of long term research and coverage effort. On the contrary, a mix of monetary and non monetary rewards based on the analyst's work ethic may allow them to resist pressures from conflicts of interest and induces a high research effort thus enhancing long-run reputation.
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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 | 0.026 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".