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
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
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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 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