Preferensi Pengunjung Mahasiswa Generasi Z Masa Kini Terhadap Atribut Learning Space di Perpustakaan Akademik
Bibliographic record
Abstract
Most of the current university students are born in Z Generations (1995-2010). Z Generations are unique, especially on their behavior and determining what they like. It included when they want to study around the area of their university. One of the most common study on universities is the academic library. The current academics library are also demanded to be able on adapting and presenting what Z generations want. The ideal academics library can accommodate the learning activities of this generation. This study aims to find the preferences of Z Generations in determining any learning space which come from the library. It also determining the frequency, duration, favorite floor and with whom visitor usually come to library. This preference refers to the theory of learning space attribute. The research method uses quantitative methods by using the survey and questionnaire of 185 students at the ITB, ITS and Unpad. The results showed that Z generations students agreed with the order of preference theory in learning space attribute. This means the academic librarys on university recently should refer to the theory of learning space attribute, so the library can increase the level of the visitors.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 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".