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Record W4220809344 · doi:10.5430/ijhe.v11n4p177

COVID-19’s Implications for Accounting Courses’ Competence Development: The Case of Portugal

2022· article· en· W4220809344 on OpenAlexvenueno aff
A. Domingos, Manuela Sarmento

Bibliographic record

VenueInternational Journal of Higher Education · 2022
Typearticle
Languageen
FieldComputer Science
TopicHealthcare during COVID-19 Pandemic
Canadian institutionsnot available
Fundersnot available
KeywordsCompetence (human resources)PortugueseLifelong learningCoronavirus disease 2019 (COVID-19)AccountingDescriptive statisticsPsychologyMedical educationPedagogyMathematics educationBusinessMedicineMathematics

Abstract

fetched live from OpenAlex

Over the past few decades, higher education in accounting has been criticized for its focus on developing technical competences at the expense of general competences. The objective of this study is to analyze the general competences developed by the final-year students of accounting courses at Portuguese public polytechnics and to compare them with the most evident ones identified through a literature review for the current accounting profession. The study adopts a quantitative approach, carried out through a survey of 137 final-year students in the 2019/2020 academic year and using the techniques of descriptive and multivariate statistics. Among the main results, it was found that the ability to use information and communication technologies and lifelong learning were the most developed general competences. The results obtained allow us to conclude that the new practices developed through distance learning facilitated the development of technological competences, producing new reflections for improving the training of accountants.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.743
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0020.000
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 head0.049
GPT teacher head0.406
Teacher spread0.357 · 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 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
Published2022
Admission routes1
Has abstractyes

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