Designing Surveys and Analysing Results from a Gender Perspective in Economic Research
Bibliographic record
Abstract
This document provides guidance on the integration of gender and diversity considerations into applied research in economics focusing on countries in which the informal sector is predominant. It draws inspiration from the support given to the West African research centres involved in researching solutions to the socioeconomic challenges posed by the Covid-19 pandemic, particularly the livelihoods of vulnerable groups and the informal sector. The document was written with the assistance of the International Development Research Centre (IDRC) and is intended to be a guide to applied research. Section 1 sets out the principal orientations of gender analyses. Section 2 examines how, in practice, considerations of gender and diversity are integrated into the design and formulation of statistical and qualitative surveys, and into their descriptive and logistic analyses. Section 3 contains a brief compilation of the resources available on gender, the informal economy, and the Covid-19 pandemic.
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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 | 0.176 | 0.240 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.017 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".