Chocs/contre choc pétroliers et investisssements étrangers au Nigéria
Bibliographic record
Abstract
The problems linked to oil profits have been the subject of numerous studies. Few of these studies, however, have dealt with the specific problem of the impact of oil price shocks and return shocks on foreign investments in revenue-generating countries. This paper seeks to analyze the case of Nigeria which has been and still is a key country for the international oligopolies, thanks to the sheer size of its market and its oil wealth. In the face of Africa 's current decline Nigeria is, with South Africa, a potential keystone state that could in time bind together other states into a regional bloc with good prospects for growth. This possible unifying role, however, seems to depend on foreign investment picking up. It is therefore essential to understand better the extent to which foreign investment was spurred from 1973 onwards by the oil boom and what the consequences were of the crisis that began in 1982.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".