MétaCan
Menu
Back to cohort
Record W10400430

An Analytic Hierarchy Process Approach For Supplier Evaluation and Selection in a Steel Manufacturing Company

2007· dissertation· en· W10400430 on OpenAlexaboutno aff
Farzad Tahriri

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldDecision Sciences
TopicMulti-Criteria Decision Making
Canadian institutionsnot available
Fundersnot available
KeywordsAnalytic hierarchy processPurchasingSelection (genetic algorithm)Operations researchProcess (computing)Supplier relationship managementAnalytic network processComputer scienceHierarchyManagement scienceIdentification (biology)Multiple-criteria decision analysisEngineeringOperations managementBusinessSupply chain managementSupply chainMarketingEconomicsArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

A nonrandom sample (N = 30) of mass murderers in the United States and Canada during the past 50 years was studied. Data suggest that such individuals are single or divorced males in their fourth decade of life with various Axis I paranoid and/or depressive conditions and Axis II personality traits and disorders, usually Clusters A and B. The mass murder is precipitated by a major loss related to employment or relationship. A warrior mentality suffuses the planning and attack behavior of the subject, and greater deaths and higher casualty rates are significantly more likely if the perpetrator is psychotic at the time of the offense. Alcohol plays a very minor role. A large proportion of subjects will convey their central motivation in a psychological abstract, a phrase or sentence yelled with great emotion at the beginning of the mass murder; but in our study sample, only 20 percent directly threatened their victims before the offense. Death by suicide or at the hands of others is the usual outcome for the mass murderer.

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)0.011
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.918
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.173
GPT teacher head0.513
Teacher spread0.340 · 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.

Study designSimulation or modeling
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

Citations1
Published2007
Admission routes1
Has abstractyes

Explore more

Same topicMulti-Criteria Decision MakingFrench-language works237,207