UPAYA PEMERINTAH KANADA DALAM MENINGKATKAN KESETARAAN GENDER DI IRAK MELALUI PENDEKATAN FEMINISME TAHUN 2017-2023
Bibliographic record
Abstract
Canada has implemented various initiatives in Iraq focused on combating gender-based violence and discrimination. In this article, Canada implemented multiple initiatives to improve women's equality in Iraq between 2017 and 2023 by launching programs designed to reduce gender-based violence and discrimination, in line with the Feminist International Assistance Policy (FIAP). This article employs a qualitative research methodology, utilizing concepts such as Gender Inequality, Feminist Foreign Policy, and the 3R framework (Women's Rights, Representation, Resources) to analyze the Canadian government's efforts toward achieving gender equality in Iraq. The findings indicate that FIAP has played a significant role in increasing Iraqi women's participation in public life and the success of initiatives targeting gender inequality. Canada's multifaceted approach has included establishing women's hotlines, providing legal consultation, and supporting women's economic empowerment. This research contributes to a deeper understanding of how feminist foreign policy can be effectively implemented to achieve gender equality in developing countries. The study demonstrates the potential of FIAP as an impactful solution for addressing persistent gender disparities in various international contexts.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".