Penyusunan Indeks Kerentanan Sosial Ekonomi Pekerja Perempuan terhadap Pandemi Covid-19 di Indonesia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it