Still Lost in Translation! A Correction of Three Misunderstandings Between Configurational Comparativists and Regressional Analysts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Even after a quarter-century of debate in political science and sociology, representatives of configurational comparative methods (CCMs) and those of regressional analytic methods (RAMs) continue talking at cross purposes. In this article, we clear up three fundamental misunderstandings that have been widespread within and between the two communities, namely that (a) CCMs and RAMs use the same logic of inference, (b) the same hypotheses can be associated with one or the other set of methods, and (c) multiplicative RAM interactions and CCM conjunctions constitute the same concept of causal complexity. In providing the first systematic correction of these persistent misapprehensions, we seek to clarify formal differences between CCMs and RAMs. Our objective is to contribute to a more informed debate than has been the case so far, which should eventually lead to progress in dialogue and more accurate appraisals of the possibilities and limits of each set of methods.
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.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it