French managers and the organization of financial economics in the 1970s and 1980s
Bibliographic record
Abstract
This article analyzes an under-studied aspect of the history of managerial thought in France: the role of French managers in the organization of financial economics during the 1970s and 1980s. While the foundations of financial economics were established by American economists in the 1960s and 1970s, these ideas were introduced in France between the second half of the 1970s and the early 1980s, mainly by French managers trained in Canada and the United States. This article describes how these managers introduced and structured this field in France, both scientifically and practically. In addition to analyzing publications and archives, this article draws on information collected through a survey and interviews with the main French actors of this period. It also uses an original database constructed from working papers of the Centre d’Enseignement Supérieur des Affaires, and original statistical data compiled from documents of the European Institute for Advanced Studies in Management. The article presents three main results. First, French managers benefited from a favorable historical context for introducing research in financial economics. Second, at that time the contributions of French managers consisted mainly in applying mainstream American models of market finance to French data, thanks to the creation of computer databases. Third, French managers largely contributed to the performativity of the financial economics in France.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".