Penyusunan Indeks Kerentanan Sosial Ekonomi Pekerja Perempuan terhadap Pandemi Covid-19 di Indonesia
Bibliographic record
Abstract
Kemunculan pandemi Covid-19 memberikan dampak negatif pada kerentanan pekerja perempuan di Indonesia. Pada situasi pandemi, pekerja perempuan semakin rentan dalam hal keberlanjutan status kerja yang berdampak buruk pada kondisi sosial dan ekonomi mereka. Dampak pandemi Covid-19 yang dirasakan pekerja perempuan dapat memunculkan permasalahan yang lebih kompleks jika tidak diberikan perhatian khusus. Diperlukan suatu ukuran yang dapat menunjukkan kerentanan sosial ekonomi pekerja perempuan terhadap pandemi Covid-19 di Indonesia. Oleh karena itu, penelitian ini bertujuan untuk menyusun Indeks Kerentanan Sosial Ekonomi Pekerja Perempuan terhadap Pandemi Covid-19 di Indonesia dengan menganalisis data hasil Survei Angkatan Kerja Nasional (Sakernas) Agustus 2021. Metode analisis yang digunakan dalam penyusunan indeks merujuk pada pedoman OECD dengan menggunakan analisis faktor eksploratori. Hasil penelitian menunjukkan terdapat 12 indikator dalam tiga faktor yaitu hak pekerja, kondisi sosial pekerja, dan kondisi ekonomi demografi pekerja. Berdasarkan nilai IKSEPP Covid-19 didapatkan provinsi dengan nilai indeks tertinggi adalah NTB dan terendah adalah Kepulauan Riau.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".