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Record W4221031357 · doi:10.7160/eriesj.2022.150106

Attitudes of Employers and University Students to the Requirements for Accountants in the Czech Republic

2022· article· en· W4221031357 on OpenAlexaboutno aff
Kateřina Berková, Lenka Holečková

Bibliographic record

VenueJournal on Efficiency and Responsibility in Education and Science · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicEducation, Psychology, and Social Research
Canadian institutionsnot available
Fundersnot available
KeywordsCzechPerceptionAccountingPsychologyQuarter (Canadian coin)GermanMedical educationBusinessMedicineGeography

Abstract

fetched live from OpenAlex

The aim of the study is to verify employers and university students’ perception of the importance of professional and soft competencies that is placed on the position of financial accountant in the Czech Republic. The study is based on the international knowledge oriented to the difference between university students and employers in perception of the importance of professional and soft competencies. The research is focused on Czech companies from two regions and students of Accounting and Finance attending universities from two different regions. The research was conducted with the help of advertisement analysis and a questionnaire survey in the first quarter of 2020. In the advertisements, mainly the information literacy and usage of English language in accounting appeared. The perception of employers is not in accordance with the importance of competencies perceived by students who would like to work in the accounting profession. Responsibility, reliability, accuracy, and independence are important for students. Differences in the perception of competencies importance were not found. This study contributes to the identification of the competency’s importance regarding employers and students. It will be necessary to innovate teaching methods with the emphasis on the effective readiness of graduates for the accounting profession.

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)0.021
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0210.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.083
GPT teacher head0.493
Teacher spread0.410 · 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.

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

Citations5
Published2022
Admission routes1
Has abstractyes

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