Sustainable Development Goals for Empowering Women Fishers Through Mangrove Use
Bibliographic record
Abstract
The Protection and Empowerment of Fishermen, Fish Cultivators, and Salt Farmers Act of 2016 protect small fishermen by requiring the government to give financial assurances if harvest yields are low.This law does not recognise or demand affirmative action for women fishermen to obtain equal access to protection and empowerment programmes.This forces women fishermen, culturally segregated from the fishing sector, into the home.Indonesia's Sustainable Development Goals (SDGs) include gender equality.This study examines gender imbalance in Law 7 of 2016's fisherman support programme in Brebes' Mangrove area.In this place, women fishermen can empower themselves through mangroves and fish farming despite Law 7 of 2016's policy vacuum.The socio-legal study examines the role of laws, rules, legal policies, and other legal systems in people's lives, including non-legal variables.In Brebes Regency, the primary concern is the lack of a statute that accommodates women fishers.This study uses socio-legal and descriptive analysis.From this research, it is hoped to learn about the implications of and not yet maximal programmes for empowering women fishermen according to Law Number 7 of 2016, which affects their economic and social life, and how these women fishermen have opportunities and equality (Gender Equality) so they can empower themselves among the people of Brebes Regency in particular and Central Java in general.
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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".