MétaCan
Menu
Back to cohort
Record W4322494791 · doi:10.32920/22183723

Developing a Code of Ethics for Disaster Tourism

2023· preprint· en· W4322494791 on OpenAlexaff
Ilan Kelman, Rachel Dodds

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsTourismContext (archaeology)Work (physics)BusinessConvergence (economics)EnforcementLaw enforcementCode (set theory)Environmental planningEthical codeRisk analysis (engineering)Computer securityPublic relationsPolitical scienceComputer scienceLawGeographyEngineeringEconomic growthEconomics

Abstract

fetched live from OpenAlex

<p>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. </p>

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.479
Threshold uncertainty score0.491

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.291
GPT teacher head0.452
Teacher spread0.161 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreOther

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

Explore more

Same topicDisaster Management and ResilienceFrench-language works237,207