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Record W2732030530 · doi:10.5539/ijef.v9n7p242

The Reflection of Social Responsibility Accounting Application in the Insurance Companies-Jordan to Increase Their Earnings

2017· article· en· W2732030530 on OpenAlexvenueno aff
Ali Mustafa Magablih

Bibliographic record

VenueInternational Journal of Economics and Finance · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsnot available
Fundersnot available
KeywordsAccountabilityAccountingRevenueBusinessSocial responsibilityEarningsIdentification (biology)Social accountingSocial insuranceSample (material)Reflection (computer programming)FinancePublic relationsAccounting information systemEconomicsPolitical scienceLaw

Abstract

fetched live from OpenAlex

Identification reflection of the researcher by the Jordanian insurance companies increasing their revenues, for the purposes of the study were the use of the descriptive approach to identify analytical and distributed on a random sample of financial managers and senior officials in those companies and the volume of the questionnaires distributed (40) Identification of recalled (40). The study came to a set of results, the most important of which are the following: there is no adequate awareness and understanding of the concept of responsibility and accountability in the insurance companies in Jordan.There is no full and effective application of the concept of responsibility and accountability in the insurance companies in Jordan.There is no understanding of the interaction of the staff of the concept of responsibility and accountability in the insurance companies in Jordan.There is a relationship between the understanding and application of social responsibility and increasing income and to find if there is a relation between the earning and the accounting for social responsibility.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.002
Scholarly communication0.0030.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.026
GPT teacher head0.286
Teacher spread0.260 · 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 source (direct Gemma or distilled Codex), 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
Published2017
Admission routes1
Has abstractyes

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