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Record W3092211778 · doi:10.5430/ijfr.v11n5p221

Financial Accounting, Analysis and Features of Calculations With Personnel

2020· article· en· W3092211778 on OpenAlexvenueno aff
Gulnara A. Gareeva, Диана Рамилевна Григорьева, Ilnur I. Mahmutov

Bibliographic record

VenueInternational Journal of Financial Research · 2020
Typearticle
Languageen
FieldEngineering
TopicErgonomics and Human Factors
Canadian institutionsnot available
FundersKazan Federal University
KeywordsPaymentBusinessEconomic shortageAccountingProfit (economics)FinanceCompensation (psychology)Actuarial scienceEconomics

Abstract

fetched live from OpenAlex

One of the main factors of the effectiveness of the enterprise is the staff. Competent accounting and analysis of calculations with staff for other operations can significantly affect the final financial activities of any organization. The calculations with the staff are not a less important component of the final report of the organization, on the basis of which the profit is formed. All this leads to whether the company conducts effective financial activity or not. The organization's calculations with employees for other operations include payments for merchandise paid by it to merchants, for goods purchased by employees with payment by installments at the expense of the credit received by the organization in the bank, on loans issued to employees. It also involves payments for individual housing construction, the purchase or construction of garden houses, the acquisition of a household. The main aim is to recover material damage caused by an employee of the organization as a result of shortages and theft of monetary and material values and other types of damage. To achieve this goal, the following tasks were solved: the theoretical basis for accounting for loans granted, for material damage compensation was considered, the company was analyzed. In this paper, we consider the main aspects of the management of personnel by other operations, their competent accounting, as well as analysis based on the average statistical data of companies in Russia (Gareeva & Grigoreva, 2019).

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.505
Threshold uncertainty score0.221

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 communication0.0000.000
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 head0.029
GPT teacher head0.304
Teacher spread0.275 · 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 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

Citations2
Published2020
Admission routes1
Has abstractyes

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