Privatization in Debtor Economies: A Conjunctural Phenomenon in World Capitalism
Bibliographic record
Abstract
While there is a large and growing literature on the privatization phenomenon of the last decade and a half, much of the theorization is limited to the argument of state versus private sector efficiencies and the advantages of free market versus state contarolled envcironments (Andic 1992; Cook and Kirkpatrick 1988; Commander and Killick 1988). Apart from this general literature on privatization, some attention has been given to the specific experience of developing countries where, in most cases, privatization means de-nationalization (Odle 1993; Commander and KIillick 1988; Craig 1988). Moreover, the link has been made between privatization of state enterprises in debtor economies and governments' ovjective of deficit financing, mainly in regard to external debt servicing (Manzetti 1993).
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".