MétaCan
Menu
Back to cohort
Record W2304477819 · doi:10.19030/jber.v4i11.2713

Using The EVA Financial Management System To Make The Wrong Decision

2011· article· en· W2304477819 on OpenAlexaff
David A. Stangeland

Bibliographic record

VenueJournal of Business & Economics Research (JBER) · 2011
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Reporting and Valuation Research
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMeasure (data warehouse)Cash flowCompensation (psychology)Economic Value AddedCapital budgetingComputer scienceTerm (time)FinanceActuarial scienceEconomicsBusinessMicroeconomicsIncentiveDebt

Abstract

fetched live from OpenAlex

EVA may result in underinvestment problems and suboptimal strategies used to boost near-term performance at the expense of the future. To solve these problems, a new measure, CFMAC, is developed. This paper describes the types of problems associated with the EVA Financial Management System that includes use of the EVA measure and compensation schemes touted by EVA proponents. A new annual measure, cash flow minus amortized capital (CFMAC) is developed to address the problems inherent to EVA.

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)0.014
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.645
Threshold uncertainty score0.837

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.244
GPT teacher head0.374
Teacher spread0.130 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Business & Economics Research (JBER)Same topicFinancial Reporting and Valuation ResearchFrench-language works237,207