Bibliographic record
Abstract
In this paper it is shown that when employees have ex post bargaining power, the entrepreneur will try to avoid technologies that are based on a large number of complementary tasks. We demonstrate that the entrepreneur can shelter profit from the employees’ rent‐seeking behaviour by raising debt. Moreover, the strategic use of debt financing can favour the adoption of technologies that rely on synergies. JEL Classification: G31, J30, L20 Dette stratégique en présence de technologies impliquant plusieurs tâches. Ce mémoire montre que quand les employés ont un pouvoir de négociation ex post, l'entrepreneur va tenter d'éviter les technologies qui sont basées sur un grand nombre de tâches complémentaires. On montre que l'entrepreneur peut protéger ses profits des activités de chasse aux rentes des employés en accroissant sa dette. De plus, l'utilisation stratégique du financement par la dette peut favoriser l'adoption de technologies qui dépendent de synergies.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".