Questioning Assumptions about Race, Social Class and Crime Portrayal: An Analysis of Ten Years of Law and Order
Bibliographic record
Abstract
Social researchers have paid significant interest to the portrayal of non-whites and members of the lower classes in both news broadcasts and the fictional crime drama. They have also explored whether social programming uses educational entertainment to positively sway public opinion or as propaganda to support the status quo. Little has been done recently to determine the representation of intra- versus interracial crime in the crime drama. Also, there is the underlying assumption that may be taken almost a priori by viewers that criminals are more often portrayed as poor and non-white and victims are more often portrayed as whites with more resources. This study utilizes the first ten year of Law and Order, an immensely successful crime drama. It explores both the portrayal of victims and perpetrators by race and social class as well as an examination of how these topics are framed and communicated to the public. Descriptive statistics are used to determine whether the portrayal by race and social class is reflective of crime rates during the decades. A content analysis is used to determine if topics that deal specifically with these factors are designed to educate, maintain the status quo, or perhaps accomplish both goals.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".