Upaya Memulihkan Hak-hak Perempuan: FIAP Kanada dalam Upaya Penurunan Gender-Based Violence di Afghanistan
Bibliographic record
Abstract
Canada as a country highly committed to promoting gender equality and women's empowerment, launched the Feminist International Assistance Policy (FIAP) in 2017 under the leadership of Prime Minister Justin Trudeau. The Feminist International Assistance Policy (FIAP) is a feminist-based foreign policy that focuses on gender equality and women's empowerment. In this case, FIAP's foreign policy is considered to be the best solution for Canada to overcome existing poverty and inequality. Afghanistan is one of the target countries for receiving Canadian FIAP assistance. Given the high percentage of Gender-Based Violence (GBV) phenomena and discrimination in Afghanistan, Canada is also trying to help reduce the level of GBV and discrimination in Afghanistan through the FIAP policy program. With this the author is interested in identifying various forms of cooperation carried out by Canada and Afghanistan in efforts to combat gender-based violence in Afghanistan, as well as explaining how a feminist perspective views Canadian FIAP policies. The scope of this research is during the period of Prime Minister Justin Trudeau's government from 2018 to 2020. This research uses qualitative methods to explain how feminist-based foreign policies can affect the level of reduction of gender-based violence, and uses congruent methods as an analytical technique. The theory of liberal feminism, Gender-Based Violence, the concept of Gender Equality, Gender Mainstreaming, and Feminist Foreign Policy will be used in this research as a knife of analysis. The results of the study show that with the Feminist International Assistance Policy (FIAP) as a manifestation of liberal feminism, Canada will promote gender equality and empower women through various existing programs and policies so that it can have an impact on reducing the level of gender-based violence in Afghanistan.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".