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Record W3120190516 · doi:10.5267/j.ac.2020.12.021

The expected impact of applying IFRS (17) insurance contracts on the quality of financial reports

2021· article· en· W3120190516 on OpenAlexvenueno aff
Ahmad Dahiyat, Walid Omar Owais

Bibliographic record

VenueAccounting · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicInsurance and Financial Risk Management
Canadian institutionsnot available
Fundersnot available
KeywordsComparabilityQuality (philosophy)BusinessActuarial scienceInternational Financial Reporting StandardsSample (material)Representation (politics)CashDescriptive statisticsAccountingFinanceStatisticsMathematics

Abstract

fetched live from OpenAlex

This study aimed to explore the expected effect of applying the International Financial Reporting Standard (IFRS) 17 Insurance Contracts on the quality of financial reports. The study followed the exploratory descriptive analytical approaches. A questionnaire was developed and distributed to a sample of 120 financial employees in all insurance companies in Jordan. It concluded that the expected impact of applying the standard on the quality of financial reports was significant, especially on the comparability of financial reports, and faithful representation. It was found that there is an expected, statistically significant and positive effect between the application of the standard, and the quality of financial reports in general, and the expected influence of applying the standard and each of comparability, faithful representation, relevance, verifiability, timely, and understandability respectively. The study recommends the application of the standard in the specified time, work to create appropriate conditions, and the need to follow objective assumptions from the company's management for the estimation of cash flows when applying the standard.

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)0.003
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 score0.174
Threshold uncertainty score0.421

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
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 head0.037
GPT teacher head0.272
Teacher spread0.235 · 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

Citations11
Published2021
Admission routes1
Has abstractyes

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