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Record W3093585640 · doi:10.5267/j.ac.2020.9.012

Key success factors in implementing international public sector accounting standards

2020· article· en· W3093585640 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueAccounting · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAccountingBusinessAccrualLegislationKey (lock)Public sectorFace (sociological concept)MarketingPublic relationsEconomicsPolitical scienceComputer scienceSociology

Abstract

fetched live from OpenAlex

There is a worldwide debate regarding the implementation of IPSAS due to the potential obstacles that may face governments when they start moving to the full accrual basis. Of equal importance is that each country has its own needs and thus it must be examined separately to find out the factors that may motivate or limit the effective adoption of IPSAS. This study, therefore, aims to explore the key success factors when it comes to adopting IPSAS in Jordan. A 39-item questionnaire was devised and sent to 500 employees who were working in public entity departments. Only 326 questionnaires were returned, yielding a response rate of 65.2%. The results suggested that the most important factors were local legislation and the infrastructure, while the training of staff came at the bottom of list. These results are important to regulators for drawing up a clear road map for transferring fully to IPSAS.

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.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.460
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.004
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.256
Teacher spread0.230 · 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