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Record W4281383152 · doi:10.2308/jiar-2021-084

Fair Value Accounting for Property, Plant, and Equipment: Impact of IFRS 1 Adoption

2022· article· en· W4281383152 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of International Accounting Research · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsYork UniversityWilfrid Laurier University
Fundersnot available
KeywordsDepreciation (economics)Historical costFair valueAccountingBusinessInternational Financial Reporting StandardsValue (mathematics)Market valueEconomicsMonetary economicsFinanceMicroeconomics

Abstract

fetched live from OpenAlex

ABSTRACT This paper uses a unique setting of Canadian public firms adopting International Financial Reporting Standards (IFRS) to investigate the factors that motivate companies to revalue Property, Plant, and Equipment (PP&E) under the deemed cost provision in IFRS 1, and whether revaluations help predict future performance, and what is the market reaction to such revaluations. Utilizing the probit model, difference-in-differences approach, and Wald test, we find that large firms and/or firms with higher net PP&E to total assets ratios are more likely to revalue PP&E, and firms adopting the fair value option for PP&E record lower depreciation in the post-IFRS period. In addition, we show that investors react negatively to the firms electing the fair value option for PP&E and the market discounts such revaluation information.

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)0.006
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.527
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0000.001
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 head0.038
GPT teacher head0.324
Teacher spread0.286 · 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