MétaCan
Menu
Back to cohort
Record W2898553701 · doi:10.5539/ibr.v11n11p176

The Level of Disclosure of Intellectual Capital at Jordanian Development Banks

2018· article· en· W2898553701 on OpenAlexvenueno aff
Firas A.N Al-Dalabih

Bibliographic record

VenueInternational Business Research · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIntellectual Capital and Performance Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsIntellectual capitalNoticeBusinessSample (material)AccountingCapital (architecture)FinancePolitical scienceGeographyLaw

Abstract

fetched live from OpenAlex

This study aims at identifying the level of disclosure of intellectual capital at the Jordanian development banks. The study sample composed of a hundred individuals working at the National Bank to Finance Small Projects around the different governorates of the Hashemite Kingdom of Jordan. A questionnaire has been prepared and distributed over the study sample. ninety five Questionnaires have been retrieved; valid for the statistical analysis purposes with a percentage of (95%). The study results showed that the level of disclosure of intellectual capital with all its dimensions (human capital, customer capital and structural capital) at the Jordanian development banks was of a high level. The results also showed that there is a high level of awareness performed by the Jordanian development banks’ employees in regard to the necessity and importance of the intellectual capital’s disclosure. The study was concluded with a number of recommendations among which were that the Jordanian development banks shall take notice toward increasing their workers’ awareness regarding the importance of intellectual capital’s disclosure, as well as applying this study over commercial and Islamic banks for the purposes of carrying out a comparison between them and the development banks.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.378
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
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.114
GPT teacher head0.332
Teacher spread0.217 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueInternational Business ResearchSame topicIntellectual Capital and Performance AnalysisFrench-language works237,207