Bibliographic record
Abstract
Recently, including traffic accidents, fire, and disaster, various incidents and serious violent crimes are gradually increasing. So, these cases tend to be legal issues. However, some cases can be examined and resolved with the help of forensic science or forensic medicine. Especially, during the criminal process, physical evidences which were gathered and analyzed scientifically have a important role. To keep the due process and protect defendant`s right successfully, rational and scientific criminal investigation is absolutely necessary. To accomplish these goals for investigators or law enforcement officers, forensic identification, forensic science or forensic medicine education program should be well established and given enough. Furthermore, it is very necessary that these courses and programs should be organized as major study in universities where criminal justice college or police administration are set up. Usually, I recognize and emphasize the importance of forensic science education at the university curriculum. So, I have analysed the actual condition of forensic science programs and courses at law schools, medical schools, and criminal justice colleges in Korea. To find out good resolutions or alternatives, I have studied these issues comparatively. In my paper, forensic science education programs which are operated in Germany, Canada, Australia, and U.S.A are introduced as instances in detail. I have focused on how to encourage and improve the forensic science education program at university level. Through this study, some alternatives are presented.
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 | 0.001 |
| 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 | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.005 |
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".