Bibliographic record
Abstract
In 1980, a handful of extremely wealthy families still controlled more than half of France’s top 200 firms. Far from being an anachronism, the way large French capitalists networked together in a combination of family ownership and interlocking directorships and their privileged access to the state were key to the success of French capitalism during the ‘30 glorious years’. Family networking was rooted in the most successful parts of French business culture. One study of 2,103 firms for which records exist in ten different French industrial centres over the whole period 1780–1935 found that 17 per cent defied the ‘clogs to clogs in three generations’ historical rule of thumb, and that there were extreme regional variations in firm longevity: in Eastern France (Alsace, Lorraine and Franche-Comte) over half the firms in 1935 were between three and six generations old, while in Normandy and Northern France the proportion was one quarter. The differences in survival rates appeared to lie in firm culture: those that survived longest appeared to stress practical techniques more, to be run by entrepreneurs with engineering and grandes ecoles (polytechnique) backgrounds, to have introduced social welfare schemes, to have a Protestant work ethic and to have remained under family control (Levy-Leboyer 1997).
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".