Bibliographic record
Abstract
With the advent of the Bill of Rights, making the offender less accessible, journalists are falling back more than ever on the victim to feed their daily tales of victimization. The author analyses the various forms this source of victimization takes; for some victims, they are generally crimes that are spectacular and violent, and are often perpetrated against the most susceptible and vulnerable victims. Each type of media (radio, dailies, weeklies, television) represents a particular way of adding to the suffering of the victim, and each has its way of “exploiting” the victim. The victim becomes a tool of the media, both commercially and ideologically, often with the connivance of the police, who also uses the victim for its purposes. The victim is portrayed in stereotype, according to the type of victimization reported and the offender implicated, creating a guilty or innocent victim, and literally depriving him of his own account of his victimization to make it an object of curiosity that sells well. Finally, the author analyzes how the police and the media, by interaction, can exploit the victim under the pretext of prevention or crime control and even through certain phenomena such as the reporting of crime waves promoting fear of crime. The article concludes that the media should have more respect for victims of crime.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.022 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".