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Record W6986853891

Relación del resultado neto del ejercicio con otros ingresos y otros gastos en una Caja Municipal de Ahorro y Crédito, 2018-2022

2024· article· en· W6986853891 on OpenAlexaboutno aff

Bibliographic record

VenueDialnet (Universidad de la Rioja) · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBusiness, Education, Mathematics Research
Canadian institutionsnot available
Fundersnot available
KeywordsNet incomePopulationSample (material)Quarter (Canadian coin)DecreeNet profitAdjusted gross income
DOInot available

Abstract

fetched live from OpenAlex

The objective is to decree how the net result of the year is related to other income and other expenses in a Municipal Savings and Credit Fund (CMAC), 2018-2022. It was based on a quantitative, transectional, descriptive and correlational approach. The population was the financial statements, the sample of 20 quarters with intentional sampling. The documentary analysis and the research sheet instrument were carried out. The result showed that the relationship between the net result of the year with other income and other expenses, in a CMAC, is inverse although not significantly. This finding arose due to dissimilar balances; That is, the increases or decreases in both variables do not coincide, from one quarter to the next. However, in the 20 quarters, most of the time the results showed inverse balances, since the net result for the year was reduced compared to the amounts achieved in pre-pandemic times, while the differential of other income less other expenses improved since They stopped being negative, after the problems of COVID-19 were overcome. It was concluded that the relationship between the net result of the year with other income and other expenses is not significant, in a CMAC, but it is weakly inverse.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.269
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.017
GPT teacher head0.285
Teacher spread0.267 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations0
Published2024
Admission routes1
Has abstractyes

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