Bibliographic record
Abstract
Cette étude propose et teste un modèle empirique des choix financiers des entreprises françaises, issu de la superposition de deux cadres conceptuels principaux, la Static Trade-off Theory et la Pecking Order Theory. En cela, cette analyse se situe dans la lignée des travaux de Fama et French (1997a) et Opler et Titman (1996). Dans ce cadre, les entreprises basent leurs décisions de financement sur leur situation par rapport au ratio cible d’endettement, estimé ici par la moyenne sectorielle. L’analyse utilise un modèle d’explication des flux financiers, non encore appliqué en France et qui ne paraît avoir été employé qu’à quelques reprises antérieurement. Elle couvre la période de 1987 à 1996 et repose sur l’examen de 2 678 observations. Trois modèles expliquent respectivement la proportion des besoins de fonds comblés par l’autofinancement, la dette totale et la dette à long terme. Il ressort que l’écart par rapport à la cible est un élément explicatif important et significatif des décisions d’endettement, mais que les variables liées à la Pecking Order Theory guident également les comportements de financement des entreprises en France. En particulier, la rentabilité et la taille influencent de façon significative le processus de retour vers la cible.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".