On the Characteristics of Ranking-based Gender Bias Measures
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
With increased recent awareness on the possible impact of retrieval techniques on intensifying gender biases, researchers have embarked on defining quantifiable gender bias metrics that can provide the means to concretely measure such biases in practice. While successful in allowing for identifying possible sources of gender bias, there has been little work that systematically explores the characteristics of these metrics. This paper argues that effective future works on gender biases in information retrieval require a careful understanding of the bias metrics in terms of their consistency, robustness, sensitivity and also their relation with psychological characteristics and what they actually measure. Through our experiments, we show that more rigorous work on gender bias metrics need to be pursued as existing metrics may not necessarily be consistent and robust and often capture differing psychological characteristics.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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