Karakteristik Kaum Homeless di Kota Semarang Untuk Memberikan Konsep Rumah Tinggal Bagi Kaum Homeless di Kota Semarang
Bibliographic record
Abstract
Cepatnya pertull1buhan kota ternyata ll1ell1bawa berbagai ll1asalah, sa lah satunya \nadalah ll1eningkatnya jUll1lah ll1asyarakat ll1i skin kota. Mereka dapat ditell1ui di \ntiap sudut kota, ll1encari pencaharian sebagai pengemis, pemulung, buruh kasar, \ndan dari se~1or informal lain \nKemi skinan tersebut dan sangat terbatasn ,3 aya beli ll1ell1aksa mereka berteduh \ndi emperan toko, dibawah jembatan didal illll pasaT tFadisional dll. \nMeskipun dell1ikian ada juga yang mencoba bertahan hi L1~ dengan membuat \n"rumah" yang diwujudkan sesuai persepsi ll1ereka. Konsep ik. ang muncul \npada bangunan tempat tinggal mereka ini cukup menarik untuk diteliti, mengingat \nrumah tersebut m reka bangun sendi[· dan dihaTapkan dapat menampung sell1ua \nkebutuhan mereka.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 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".