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Record W2962505879

The Adoption of Cost-Benefit Analysis as an Indicator for Evaluate the Accounting Performance of Business Entities(The Case Study for Canadian Railway)

2019· article· en· W2962505879 on OpenAlexaboutno aff
Ali Qasim Hasan Al-Obaidi, Amal Mohammed Salman

Bibliographic record

VenueAcademy of Accounting and Financial Studies journal · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicAviation Industry Analysis and Trends
Canadian institutionsnot available
Fundersnot available
KeywordsAccountingSample (material)Management accountingOrder (exchange)BusinessCost accountingProcess (computing)Set (abstract data type)Test (biology)Cost–benefit analysisActuarial scienceOperations managementEconomicsFinanceComputer science
DOInot available

Abstract

fetched live from OpenAlex

This study tested the relationship between the total cost and expected benefits to the project that proposed by top management of company selected. It was based on a key hypothesis that states, the outcome of proper and accurate analysis of the cost-benefit relationship can be considered as an indicator that can be used to assess the financial benefit of the proposed project and at the same time evaluation the accounting performance of the accountants who responsible for carrying out this process. The study contributed to formulating a group of objectives, in order to achieve the main objective of the study and test the main hypothesis, a study sample was chosen (Canadian Railway Co.) by examine a certain case in it related to study. Finally, the study reached a number of conclusions and recommendations, most importantly are, the application of the concept CBA in a proper manner will help the company to obtain a set of benefits, not limited to the assessing of the performance of accountants within company but expanded as to include the encouraging the accounting authorities within country in the selection of best accounting practices and applications that enhancing the accountants duties in future.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.053
GPT teacher head0.296
Teacher spread0.244 · 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 designObservational
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

Citations3
Published2019
Admission routes1
Has abstractyes

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