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Record W1554635307 · doi:10.1111/1468-5973.12059

<i>Ad hoc</i>Rules, Rights, and Rituals: The Politics of Mass Death

2014· article· en· W1554635307 on OpenAlexaff
Joseph Scanlon, Christopher Stoney

Bibliographic record

VenueJournal of Contingencies and Crisis Management · 2014
Typearticle
Languageen
FieldPsychology
TopicGrief, Bereavement, and Mental Health
Canadian institutionsCarleton University
Fundersnot available
KeywordsPoliticsAccountabilityTransparency (behavior)TerrorismFlexibility (engineering)GlobalizationLaw and economicsPolitical scienceOrder (exchange)LawPublic administrationPublic relationsBusinessEconomics

Abstract

fetched live from OpenAlex

In spite of the growing incidence of mass death incidents due to factors such as climate change, technology, terrorism and globalization there are relatively few rules and procedures in place to deal with the dead and where rules do exist they are often ignored or broken. Furthermore, because of the very different cultural, legal, financial, and religious nature of states and victims international agreements, conventions and best practices are difficult to establish. This paper highlights the often political and ad hoc nature of the decisions that have to be made by responders and the challenges this presents for policy‐makers as well as the families of the victims. While accepting the need for flexibility in emergency situations, the paper identifies some of the areas where public policy reforms and frameworks are most needed in order to establish clearer rules and procedures for dealing with the dead. Finally, the paper calls for the policies and procedures that do exist to be informed by and subject to the same principles of public administration, such as accountability and transparency, that govern other areas public policy.

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.082
Scholarly communication0.0120.006
Open science0.0010.006
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0040.001

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 head0.021
GPT teacher head0.297
Teacher spread0.277 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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

Citations4
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Contingencies and Crisis ManagementSame topicGrief, Bereavement, and Mental HealthFrench-language works237,207