Norman Schofield & Gonzalo Caballero (Eds.), The Political Economy of Governance: Institutions, Political Performance and Elections
Bibliographic record
Abstract
This edited conference volume is another from Springer on the Studies in Political Economy under the editorship of Prof. Norman Schofield, the Dr. William Taussig Professor of Political Economy Professor, Department of Political Science at Washington University. Political Economy is a fast growing field that uses the logic of economics to study issues pertaining to politics and governance and of which Prof. Schofield has been one the most prolific contributors over the last four decades. This volume brings together the papers presented at the Political Economy of Governance, Institutions and Elections workshop that took place in Baiona, Spain in April 2014. Political Economy is a vast and growing field as such this volume is only representative of the many issues and the modelling techniques—both theoretical and empirical—used to address them. The topics and issues addressed in this volume span a great variety of subjects covering—as the title indicates—issues dealing with governance, institutions and elections. The chapters in this volume are not only innovative, but they challenge and engage the reader into thinking more deeply about the issues addressed. One of the major characteristic of this volume is that most papers directly or indirectly contest the existing body of knowledge by either providing alternatives ways of thinking about a problem not addressed in the main stream literature or by studying issues that have up to know been ignored in the literature. This review is organized as follows. Section 1 gives an overview of how institutions work or change over time within a country; Section 2, those dealing with different aspects of democracy and Section 3 those dealing with the workings of elections. Concluding comments are given in Section 4.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".