Kemas Ulang Informasi dalam Pembuatan Buku Pintar Siaga (Studi Kasus: Pada Kantor Badan Penanggulangan Bencana Daerah Sumatera Barat)
Bibliographic record
Abstract
AbstractDisaster is an event or series of events that threatens and disrupts people's lives and livelihoods caused by natural factors and non-natural factors and human factors resulting in human casualties, environmental damage, property losses, and psychological impacts, a phenomenon of life humans who cannot be known exactly when it happened. In facing disaster preparedness in Indonesia, there is still a lack and lack of hate education so that there is a lack of public knowledge in post-disaster planning and their readiness to anticipate disasters. Therefore, repacking information is packing information back, or changing from one form of information, to a symbol that is interpreted as a message, recorded as a sign, or sent as a signal. Preparing knowledge about disaster preparedness or hate from an early age to people who are vulnerable to disasters and in preparing themselves for disasters.Keywords: information, repackaging, disaster
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 | 0.000 | 0.001 |
| 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 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 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".