Bibliographic record
Abstract
Problems and Methods in the Study of Politics, Ian Shapiro, Rogers M. Smith and Tarek E. Masoud, eds., Cambridge: Cambridge University Press, 2004, pp. xi., 419. This important volume recounts a meeting of some the best minds in political science, but, in the end, it is a meeting in the physical sense (as the volume comes out of a conference held at Yale in 2002) and not really in any intellectual sense. The ostensible goal of the volume is to proffer answers to what the editors call “a fundamental question about the proper place of problems and methods in the study of politics…. Which should political scientists chose first, a problem or a method?” (1). Unfortunately, a good many of the contributors to the volume ask whether this is a question at all. Perhaps unsurprisingly, most of those who reject the question do not have objections to the increased technical and mathematical nature of modern political science. And, equally unsurprising, those who suggest that method has too often come before problem are those who have earlier, and often eloquently, bemoaned the rise of rational choice theory and econometric applications. As an intellectual rapprochement, the work fails. It rather resembles a dinner of extended family, where long-held differences and grievances are kept just under the breath, but as a collection of essays by leading scholars which consider the methodologies and epistemologies of political science, the volume is a smashing success.
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 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 | 0.036 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.027 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".