Bibliographic record
Abstract
The paper examines the media coverage of crime in the Macedonian printed newspapers and the way in which certain circumstances related to the offender, the victim and the crime are depicted. The content analysis is based on quantitative and qualitative data collected from the media articles within three-month period by using written coding instrument. The main findings indicate that there are different approaches to and prioritization of criminal offences worth publishing in daily newspapers. The latter exhibit uneven representation of violent compared to property crimes, which means that the former crimes compared to property felonies, although less frequent in occurrence, note higher representation in part of the newspapers. In addition, differences according to the place of crimes commission are noticed between media in Macedonian and Albanian. Albanian media tend to emphasize crime perpetrated in areas where majority population is of Albanian origin compared to other major municipalities in the country. In this way, one gets the impression that these areas are incomparably much more burdened with crime, which makes them less safe than other major municipalities. The findings relate only to media texts published in the printed newspapers on the research topic and to the classifications and definitions contained in criminal laws. Therefore, in-depth analysis on crime news depiction, not only in printed media, but also in electronic media in general, and at the web portals, in particular is challenging for scholars. This will bring new scientific insight into Macedonian criminological critical thoughts related to the role and meaning of the media and their discourse in construction of crime and criminality.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".