PENGARUH EFEKTIVITAS PROGRAM GERAKAN KESETARAAN \nGENDER MELALUI PEMBANGUNAN KELOMPOK USAHA PEREMPUAN \n(GETAR PESONA) TERHADAP KUALITAS PEMBERDAYAAN \nANGGOTA KELOMPOK USAHA GETAR PESONA \n(STUDI PADA KUP KECAMATAN MARTAPURA OKU TIMUR)
Bibliographic record
Abstract
Penelitian ini bertujuan untuk mengetahui pengaruh efektivitas program gerakan \nkesetaraan gender melalui pembangunan kelompok usaha perempuan terhadap \nkualitas pemberdayaan anggota kelompok usaha Getar Pesona. Metode penelitian \nyang digunakan adalah kuantitatif melalui strategi penelitian survai. Populasi dalam \npenelitian ini adalah anggota kelompok usaha Getar Pesona dengan jumlah \nsebanyak 34 orang. Sampel yang digunakan pada penelitan ini menggunkan tipe \nsampel jenuh dengan menurunkan seluruh populasi. Pengujian hipotesis dilakukan \nmenggunakan rumus uji t dan uji F menggunakan alat hitung SPSS adapun \npengolahan data mengguakan teknik analisis statistik regresi linier sederhana. Hasil \nanalisis yang diperoleh terdapat pengaruh antara efektivitas program Getar Pesona \nterhadap kualitas pemberdayaan anggota Getar Pesona dengan kadar determinasi \nsebesar 0,637 atau 63,7%, serta sisanya sebesar 36,3% dipengaruhi oleh variabel \nlain diluar penelitian ini. \nKata Kunci :Pemberdayaan Perempuan, Program Getar Pesona, Transformasi \nSosial
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.053 | 0.008 |
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".