Bibliographic record
Abstract
Preface When the editors of the Strategies for Social Inquiry Series at Cambridge University Press first approached us to write a book on process tracing, our response was “yes, but …” That is, we absolutely agreed there was a need for such a book, but, at the same time, we were leery – hence that “but” – of writing a standard methods text. Of course, process tracing is a method, so there was no getting around writing a methodology book. Yet, from our own experience – be it working with Ph.D. students, reviewing manuscripts and journal articles, or giving seminars – we sensed a need, indeed a hunger, for a slightly different book, one that showed, in a grounded, operational way, how to do process tracing well. After discussions (and negotiations!) with the series editors, the result is the volume before you. We view it as an applied methods book, where the aim is to show how process tracing works in practice, using and critiquing prominent research examples from several subfields and research programs within political science. If the last fifteen years have seen the publication of key texts setting the state of the art for case studies, then our volume is a logical follow-on, providing clear guidance for what is perhaps the central within-case method – process tracing.
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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.481 | 0.272 |
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".