Fuzzy Set or Fuzzy Logic? Comparing the Value of Qualitative Comparative Analysis (fsQCA) Versus Regression Analysis for the Study of Women's Legislative Representation
Bibliographic record
Abstract
In this article I compare the results of Qualitative Comparative Analysis (fsQCA) applied to a medium-sized data set on women's legislative representation in Asian and Latin American countries to those of regression analysis based on the same data set. I find that both methods are suboptimal. Explaining the outcome of high women's representation, fsQCA suggests complex configurations of conditions with low empirical coverage and high sensitivity to coding. While, not without shortcomings, OLS regression analysis performs somewhat better than fsQCA. On the one hand, this method identifies two statistically significant and substantively relevant variables (i.e. quota rules and communist regimes), which strongly increase the percentage of women deputies. On the other hand, the model's interpretation is not completely clear cut, as scholars may disagree over the relevance of the one marginally statistically and substantively significant variable, the longevity of democracy.
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 | 0.088 | 0.213 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".