Virtual Detectives: Exploring Homicide Investigations and the Black Dahlia with <i>LA Noire</i>
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article explores how I use LA Noire , an open-world detective video game, to teach students about homicide investigations. Specifically, I use the video game to dive into the unsolved murder of Elizabeth Short, who was posthumously nicknamed the “Black Dahlia” in newspapers, as a central case study. In LA Noire , the backdrop of Elizabeth Short’s murder highlights the complexities of detective work. It is complemented by other in-game cases inspired by real murders that police suspected were connected. These interactive missions provide an interactive way to discuss the difficulties of homicide investigations, the media’s sensationalism of violent crimes, and the collaborative nature of institutional responses. By moving beyond mere entertainment, video games like LA Noire can serve as powerful tools for enhancing student learning and engagement.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it