1. Gender Bias in Policy Making
Bibliographic record
Abstract
Gender Bias in Policy Making"If you are basing your evidence on unrepresentative, biased samples then you cannot believe a word.In fact, it is worse than knowing nothing.Knowing things that are not so is worse than knowing nothing at all." 1 (Norman Glass)The ways "we know" and the "consequences of bias in evidence" 2 within these ways of knowing have been identified by researchers around the world as one of the main dangers to sound policy advice and good policy outcome.Experience with international impact assessments (IA) implementation suggests that not having any impact assessment might be better "than to have a bad one." 3 Sound public policy advice depends on many multifaceted, intertwined factors.Some argue that the current practice of policy advising in public administration is too reductive and fails to integrate a multiplicity of important perspectives and democratic obligations, i.e., a gender equality perspective.Others question its practicability and whether sound policy advice is even possible.This book is concerned with those tensions, and with the various ways of knowing and creating knowledge for and by public governance through impact assessment, with a specific focus on gender equality governance. ReseaRch Motivation, Questions and stRuctuReThe adoption of a gender lens in policy analysis represents an attempt to account for and overcome gender bias and to inform better, more effective policy and programme making, resulting in gender equity in accordance with human rights frameworks, including gender equality.Gender specific policy and programme analysis tools such as Gender-based Analysis (GBA) in the Canadian federal government and Gender Impact Assessment (GIA) in the European Commission in all their various forms have been introduced as analytical tools in the context 1 | United Kingdom 2006, 52.Norman Glass was the Director of the National Centre for Social Research in the United Kingdom. 2 | United Kingdom 2006, 51.Evidence is very broadly understood as "the knowledge derived from research" (Grey 1997, 1). 3 | Renda 2006, 135.294 | As in the EU's integrated guidelines, compare chapter 4.2.3.For an assessment of gender in IA practice, compare the Commission IA screening in sub-chapter 4.4.6.3 and Annex V. 295 | Stacey/Thorne 1985.296 | MacRae 2010.297 | Despite this fact, some literature, eager to promote the horizontal social clause as in Art. 9 of Treaty on the Functioning of the European Union, regards gender mainstreaming in the integrated IA as a role model for fostering social clause mainstreaming (Vielle 2012).
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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 | 0.023 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.031 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".