Bibliographic record
Abstract
Abstract Dark tourism researchers who examine sites of death, suffering, and despair have generated a significant amount of research over the past two decades. Different ways of conducting dark tourism research are emerging. These include studies oriented toward making sense of the supply and demand for such excursions, and research that explores how cultural meanings are negotiated at these destinations. There are also critiques of the wide-ranging application of the dark tourism concept, which has led some scholars to argue that it is analytically imprecise. New directions for future dark tourism research have also been proposed, including a call to shift away from discipline-centered analyses. Engaging with these developments, we suggest that the future direction of dark tourism research should involve grounding such studies in the concerns and insights offered in specific social science disciplines, including criminology and criminal justice studies among others, to add focus and precision to cross-disciplinary debates. To do so we draw from the emergence and development of penal tourism research, which examines how cultural representations of penality shape and are shaped by the practice of punishment in given societies. Since penal tourism research tends to focus on prison museums, we propose future directions for the study of this phenomenon rooted in criminological concerns for understanding how penal meaning making, including definitions of acts that are criminalized and what constitutes (in)justice, takes place in other sites of punishment memorialization including police and courthouse museums. Other future research directions include studying sites that memorialize corporate and state harms.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.044 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".