Gender Budgeting Efforts: Latin America and Canada
Bibliographic record
Abstract
In Latin America, gender inequality in education, health, and employment opportunities, among other areas, is long-standing, not because of isolation, as in other regions, but due especially to poorly designed economic policies and dis- crimination based on social class, age, ethnicity, sexual preference, and religious belief.Inequality has permeated five centuries of racial, ethnic and gender-based discrim- ination in the region, in societies where people are divided into first- and second-class citizens. It has permeated a modernization process built on the back of the worst income distribution in the world (ECLAC 2010).In Canada, by contrast, even though no official policy of gender budgeting exists, gender equality is advancing and gender budgeting work is crucial for pressing for government expenditure contributing to the removal of inequalities.Latin America has made progress on gender equality in recent decades, how- ever, as is evident by improvements in the Gender Development Index (GDI) (Figure 5.1).1 In every country in the region, the GDI was higher in 2013 than in the early 1990s, reflecting several changes that include the adoption of legisla- tion on equality between women and men and evolution in the institutions of government to reflect this legislation, as well as increasing representation of women in parliament and even women in the presidency in some countries...
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.018 | 0.003 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".