Bibliographic record
Abstract
This study focused on Jargon used by Police in Namo Rambe Police Quarter. The objectives of this study were to find out kinds of jargon used in Namo Rambe Police Quarter, to describe the meaning of jargon related to denotative and connotative meaning, and to find out the reasons of policemen using jargon in their communications. The data were limited on Jargon used in Namo Rambe Police Quarter. This study was conducted by using descriptive qualitative method. The data were taken from the conversations of policemen in Namo Rambe Police Quarter and gathered descriptively. The findings of this study show that kinds of jargon used in Namo Rambe Police Quarter classified into three categories, namely sandi huruf (Letter Codes), sandi angka (Number Codes), and Sandi Pangkat Kesatuan (Corps Position Codes). There are 49 words that are considered as jargon in Namo Rambe Police Quarter. 13 (26,53%) has denotative meaning and 5 words (10,20%) has connotative meaning. Furthermore, there are 31 words as jargon (63,27%) that can not be analyzed semantically whether they have denotative or connotative meaning. The reasons of policemen using jargon in their communications in order to keep the secret and the identity of them, to keep communication short and concise, and to make easier in delivering informations.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".