Omar Fassal, 2016, Une histoire de la fraude financière , Montréal, Liber, 226 p.
Bibliographic record
Abstract
Cet article utilise les données de 53 pays africains pour examiner la contribution des investissements directs étrangers (IDE) à l’industrialisation du continent. La période d’étude va de 1975 à 2014. La technique d’estimation repose sur la méthode des moments généralisés en système. Les résultats des différentes estimations économétriques montrent que : (i) les IDE contribuent de manière significative à la valeur ajoutée de l’industrie au PIB et non à l’emploi du secteur industriel ; (ii) l’effet positif et significatif est observé dans quatre sous régions sauf en Afrique de l’Est ; (iii) en utilisant un indice composite d’industrialisation, la contribution des IDE est très élevée. De ces résultats, il découle deux recommandations de politique économique. La première consiste à renforcer l’investissement privé de manière à accroître sa participation à la transformation structurelle du continent. La deuxième consiste à construire des indicateurs spécifiques de l’industrialisation de l’Afrique.Codes JEL : E23, F21, F41, L71, L73, L74
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.007 |
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".