Measuring the Gender-Responsiveness of Free Trade Agreements: Using a Self-Evaluation Maturity Framework
Bibliographic record
Abstract
In the recent years, we have witnessed a sharp increase in the number of free trade agreements (FTAs) with gender-related provisions. The key champions of this evolution include Canada, Chile, New Zealand, Australia and Uruguay. These countries have proposed a new paradigm, i.e. a paradigm where FTAs are considered vehicles to achieving the economic empowerment of women. This trend is spreading like a wild-fire to other parts of the world. More and more countries are expressing their interest in ensuring that their FTAs are genderresponsive and not simply gender-neutral or gender-blind in nature. The momentum is on, and we can expect many more agreements in the future to include stand-alone chapters or exclusive provisions on gender issues. This article is an attempt to tap into this ongoing momentum, as it puts forward a newly designed self-evaluation maturity framework to measure gender-responsiveness of trade agreements. The proposed framework is to help policy-makers and negotiators to: (1) measure gender-responsiveness of trade agreements; (2) identify areas where agreements need critical improvements; and (3) receive recommendations to improve the gender-fabric of trade agreements that they are negotiating or have already negotiated. This is the first academic intervention presenting this type of gender-responsiveness model for trade agreements.
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.
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 | 0.074 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".