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Record W6980098549

Austerity measures 2024 and impacts on companies

2025· dissertation· cs· W6980098549 on OpenAlexaboutno aff

Bibliographic record

VenueDigitální knihovna Univerzity Pardubice (Univerzity Pardubice) · 2025
Typedissertation
Languagecs
FieldBusiness, Management and Accounting
TopicTaxation and Legal Issues
Canadian institutionsnot available
Fundersnot available
KeywordsAusterityQuarter (Canadian coin)Government (linguistics)
DOInot available

Abstract

fetched live from OpenAlex

Diplomová práce je zacílena na identifikaci a zhodnocení změn v oblasti daně z příjmů právnických osob a vyhodnocení dopadu na konkrétní společnost. Legislativní změny mají významný dopad na obchodní společnosti, a to především na finanční situaci a administrativní zátěž. Teoretická část je zaměřena na konstrukční prvky daně, vymezení pojmů souvisejících s daní z příjmů právnických osob a metodiku výpočtu této daně. V praktické části je provedeno porovnání těchto dvou období a vyhodnocení dopadů na konkrétní obchodní společnost.

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 categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.691
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0030.003
Science and technology studies0.0020.001
Scholarly communication0.0040.006
Open science0.0020.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.002

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 head0.025
GPT teacher head0.250
Teacher spread0.225 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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