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Record W4387309546 · doi:10.52609/jmlph.v3i3.85

Disaster Cluster Approach: A Study of A New Model of Disaster Response

2023· article· en· W4387309546 on OpenAlexvenueno aff
Maktoom Almalki, Majed Alwahabi, Salem Alammi, Yousef Alawad, Sharafaldeen Bin Nafisah

Bibliographic record

VenueThe Journal of Medicine Law & Public Health · 2023
Typearticle
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsnot available
Fundersnot available
KeywordsDisaster responseEmergency responsePreparednessEmergency managementDisaster preparednessMedical emergencyCluster (spacecraft)Unit (ring theory)Operations managementComputer scienceBusinessOperations researchMedicinePsychologyPolitical scienceEngineering

Abstract

fetched live from OpenAlex

The Journal of Medicine, Law and Public Health (JMLPH) is a peer‑reviewed, open‑access journal publishing interdisciplinary research at the intersection of clinical medicine, health law, ethics, and public health 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)0.007
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0030.004
Scholarly communication0.0030.004
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.300
GPT teacher head0.469
Teacher spread0.169 · 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 designTheoretical or conceptual
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
Published2023
Admission routes1
Has abstractyes

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