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Record W2152753205 · doi:10.1504/ijem.2007.013992

Development and evolution of a crisis support service at an international airport

2007· article· en· W2152753205 on OpenAlexaffabout
Cheryl Regehr, TED BOBER, Deane Johanis

Bibliographic record

VenueInternational Journal of Emergency Management · 2007
Typearticle
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsOntario Medical AssociationUniversity of Toronto
Fundersnot available
KeywordsInternational airportEmergency managementService (business)Work (physics)Crisis managementPublic relationsBusinessDisaster recoveryAviationIncident managementOperations managementAeronauticsMarketingEngineeringPolitical scienceTransport engineeringComputer securityComputer science

Abstract

fetched live from OpenAlex

In response to increasing concerns about the impact of disaster recovery work on responders, Pearson International Airport (PIA) in Toronto established Canada's first airport based 'critical incident stress team' in the fall of 1989. The original aim was to support the local airport community particularly in the event of an air disaster. Over the 17 years of operation, the goals and services of the team have shifted, founded on emerging theory and evidenced based practices and in keeping with the changes in emergency management systems. The service component of the team expanded beyond emergency workers and airport personnel to families and friends of those affected by disaster; the nature of the service became broader and more prevention focused. In addition, a research and training component has been added with the intent of better addressing the needs of those affected by disaster.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.001
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.067
GPT teacher head0.435
Teacher spread0.368 · 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 designQualitative
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

Citations0
Published2007
Admission routes2
Has abstractyes

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