fuzzy sets … too fuzzy to study women’s representation in parliament!
Bibliographic record
Abstract
In this rebuttal piece to Buche et al , I reiterate my criticism of fuzzy set analysis as a method that is poorly suited to study women’s representation in parliament and other rather complex phenomena. The use of fuzzy set analysis is problematic from the onset, because this method asks the researchers to distinguish meaningful from non-meaningful variation and set benchmarks for high women’s representation, the cross-over point and low or non-high representation. Yet, in the study of women’s representation, the distinction between meaningful and non-meaningful variation and the setup of these benchmarks is problematic if not impossible, even with case-specific knowledge. For example, in Asia and Latin America can we talk about high women’s representation if there are 30 per cent women deputies, 35 per cent women deputies or 40 per cent women deputies? Neither the literature nor Buche et al give an answer to this question. This problem of arbitrarily setting benchmarks is magnified by the non-robust findings and low coverage of this method. It is disturbing if we get a completely different combination of conditions, if we slightly change the benchmark for high women’s representation and/or that of some of the conditions or independent variables. Because of these reasons, researchers should refrain from using fuzzy set analysis, when explaining variation in the number of deputies in parliament.
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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 | 0.024 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.005 | 0.015 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".