fuzzy sets … too fuzzy to study women’s representation in parliament!
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
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.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it