EIGHT ASPECT DESIGNS OF BOARDING SCHOOL BASED ON PREFERENCE IN NEW NORMAL ERA OF COVID-19, KENDARI CITY AND BANDUNG AS CASE STUDIES
Bibliographic record
Abstract
Pembangunan indekos mahasiswa perlu didukung oleh pedoman tertentu yang mampu menghadapi situasi dimasa new normal. Pedoman perancangan tersebut perlu mempertimbangkan pendapat mahasiswa sebagai pengguna yang mengalami langsung kejadian pandemi Covid-19 ini, agar memproleh hasil yang ideal. Bagaimanapun juga, pedoman perancangan indekos di kota Kendari yang mempertimbangkan masa new normal belum pernah dilakukan. Tujuan penelitian ini adalah untuk mengetahui preferensi mahasiswa terhadap indekos yang ingin ditinggali di masa new normal. Penelitian ini dilakukan secara kualitatif dengan pendekatan grounded theory yang bersifat ekploratif. Pengumpulan data dilakukan dengan membagikan kuisioner online yang bersifat (open-ended). Kuisioner dibagikan secara bebas (non-random sampling). Terdapat 158 responden yang mengisi kuisioner online tersebut. 128 dari yang tinggal di kota Kendari dan sisanya berada di kota Bandung. Data dari hasil kusioner yang didapatkan kemudian dianalisis dengan analisis isi. Dalam analisis isi, dilakukan dua tahapan yaitu open coding dan selective coding. Hasil penelitian menunjukkan bahwa ada delapan aspek yang menjadi preferensi indekos yang ingin ditinggali dimasa new normal. Penelitian ini diharapkan dapat menjadi kriteria rancangan indekos yang dapat mewadahi keinginan mahasiswa dan lebih antisipatif
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".