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Record W2304770025 · doi:10.5539/ijef.v8n4p306

Analytical Hierarchy Process as a Tool for Investment Appraisal

2016· article· en· W2304770025 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueInternational Journal of Economics and Finance · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicStrategic Planning and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsAnalytic hierarchy processPairwise comparisonSelection (genetic algorithm)Context (archaeology)HierarchyTask (project management)Investment (military)Computer scienceProcess (computing)Operations researchActuarial scienceQuantitative analysis (chemistry)Management scienceFinanceBusinessEconomicsEngineeringArtificial intelligenceManagementPolitical science

Abstract

fetched live from OpenAlex

<p>Analytic Hierarchy Process (AHP), is the method of multiple-criteria decision analysis. AHP; severity of the important criteria in deciding the pairwise comparisons and it carries out the sequence of decisions alternatives. AHP is a powerful and easy to understand method that allows combining qualitative and quantitative factors in the decision making process for groups and individuals.</p><p>AHP in finance is a frequently used method especially financial performance appraisal, credit appraisal, the financial failure prediction, estimation of the exchange rate and the selection of projects. Evaluation and selection of projects is a difficult task before making investment decisions. In this context, it has been developed a case study to determine the best project by applying the AHP technique. For this purpose, considering four criteria, the best projects between options have been identified and evaluated by alternative four projects.</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.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.649
Threshold uncertainty score0.187

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.022
GPT teacher head0.277
Teacher spread0.255 · 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