Bibliographic record
Abstract
Penelitian ini dilatarbelakangi dengan adanya perencanaan penggusuran bangunan yang akan dilakukan di Pasar Tarandam.Penelitian ini bertujuan untuk mendeskripsikan faktor penyebab dan bentuk-bentuk perlawanan yang dilakukan oleh pedagang Pasar Tarandam terhadap kebijakan penggusuran.Metode yang digunakan pendekatan kualitatif, tipe penelitian studi kasus.Pemilihan informan dilakukan secara purposive sampling dengan jumlah 11 informan.Pengumpulan data secara observasi, wawancara mendalam, dan studi dokumentasi.Data dianalisis dengan teknik analisis interaktif Miles dan Huberman dengan cara reduksi data, penyajian data dan penarikan kesimpulan.Hasil penelitian menunjukkan adanya faktor penyebab terjadinya perlawanan serta tindakan perlawanan yang dilakukan oleh pedagang Pasar Tarandam terhadap kebijakan penggusuran.Faktor penyebab terjadinya perlawanan pedagang yaitu (1) ekonomi subsistensi, (2) adanya harapan terhadap kelompok superordinat, (3) masa sewa kios yang belum habis, (4) pelanggan sudah tetap.Sedangkan bentukbentuk perlawanan terbagi 3 yaitu (1) perlawanan tertutup terdiri dari ekspresi terhadap penggusuran dan mengaabikan peringatan , (2) perlawanan semi-terbuka dengan mendukung pedagang yang melaukakn perlawanan, dan (3) perlawanan terbuka dengan melibatkan kelompok superordinat dan tetap berjualan ditempat tersebut.Berdasarkan penelitian tersebut peneliti menemukan adanya hal yang menarik yaitu pedagang yang berusaha melakukan tindakan perlawanan atas
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.123 | 0.038 |
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".