Bibliographic record
Abstract
In November 2016, the Canadian Minister of Finance requested an analysis from Credit Suisse on the potential impacts of privatizing the nation's airports. That same month, his German counterpart Wolfgang Schäuble proposed raising government revenues by selling a minority stake in the Autobahn network. Such proposals are striking in their own right, but even more so as indicators of the extent that privatization has taken hold in the developed world. Is there really nothing left to sell but the farm—and, more importantly, how did we get to this place? Though relatively slim, this new text by Herbert Obinger, Carina Schmitt, and Stefan Traub (henceforth ‘OST’) gives important insights into these questions. The central aim of the book is to uncover the determinants of privatization and the pace at which it occurs. Towards this end, the authors provide a comparative, empirical study of privatizations in 20 OECD countries between 1980 and 2007. Post-Communist countries are excluded, as well as states that cannot be considered high-income democracies, such as Mexico and South Korea. Following a brief summary and outline of the text in Chapter 1, OST discuss the historical reasons for the initial development of state-owned enterprises, from economic modernization to left-wing ideology. They place particular emphasis on the role of the military in building ‘strategically important industries in manufacturing…mining…and energy’ (p. 11). Conversely, in Chapter 3, the authors review the existing literature documenting the dissolution of state-owned enterprises. Examining a wide array of explanatory variables, OST conclude that low long-term GDP growth most strongly correlates with increased privatization. While the ideology of the party in power influenced the extent of privatization in the 1980s, there is limited evidence for its importance since. Additionally, the authors suggest that public deficits do not have a strong correlative effect on privatization except in European countries subject to the 1992 Maastricht Treaty, which may have forced them ‘to accelerate divestiture in order to meet the convergence criteria’ (p. 34).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".