Bibliographic record
Abstract
In 2016, I, along with other members of the Deviant Leisure Research Network, attended the American Society of Criminology Conference in New Orleans, Louisiana. During our time in New Orleans, there were certainly plenty of ‘dark tourism’ experiences and observations that were of interest to a band of critical criminologists interested in crime, harm, and commodified leisure. There were the obvious seductions and temptations of Bourbon Street and the French Quarter, in which many of us enthusiastically immersed ourselves. We toured around the fascinating and eerie ‘Museum of Death’ and observed racial abuse and sexual harassment associated with the tradition of ‘flashing’ in exchange for Mardi Gras beads (Redmon, 2015). If you drifted just a few blocks outside of the traditional tourist locations, the scars of hurricane Katrina were still visible. Plots where properties destroyed by the hurricane once stood remained empty, while others that were damaged stood derelict and unrepaired. In many ways, it was the ideal location for a criminology conference interested in crime, deviance, inequality, and harm.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".