Bibliographic record
Abstract
This paper provides a first discussion of the advantages and concerns of disaster tourism along with an initial step towards a code of ethics. Based on existing disaster and tourism codes, four guidelines are suggested and critiqued: 1. Priority in disasters should be given to the safety of disaster-affected people and responders, encompassing rescue and body recovery operations. 2. One individual should not put another individual at increased risk without consent. 3. The authorities in a disaster-affected area and their rules and regulations should be obeyed within reason. 4. Any donations or assistance offered to disaster-affected areas should be considered within the local context and should also involve nearby but non-disaster-affected communities. Targets, training, monitoring, enforcement, and evaluation for the code are also discussed along with the need for consultative processes for further developing and implementing the code. Three main areas of disaster tourism research are proposed for further work: disaster recovery, convergence behaviour, and supporting disaster risk reduction rather than post-disaster actions.
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 | 0.114 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.029 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.012 | 0.018 |
| Insufficient payload (model declined to judge) | 0.003 | 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".