Discussion about Legalization of the Private Investigation - focused on Legislative Bills of the 20th Assembly -
Bibliographic record
Abstract
The private investigation means that the individuals or the corporation(ex. private firms), as private status, investigate the facts or cases requested by ordinary citizens on behalf of national organizations(ex. police etc.). Since the private investigation began in the 19th century, it has been assumed a leading place among private security in most other countries, U.K., France, U.S., Canada and Japan etc. In these countries the private investigation is defined by laws and regulations. However our country don’t have laws and regulations related to the private investigation. So, the private investigation by detective agency or errand center etc. has been going on through the illegal or inappropriate methods and process. Therefore, also in our country, the private investigation system should be introduce to control such illegal or inappropriate private investigation. And we have to immediately enact the law as to the private investigation. So, I compared and weighed two legislative bills submitted to the 20th Assembly for legislation of private investigation., and suggested desirable ways in making a law as to private investigation. And I examined about legislative system of private investigation act, name of private investigator and organization for management and supervision of private investigation business.
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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 | 0.034 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.016 | 0.019 |
| Insufficient payload (model declined to judge) | 0.004 | 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".