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Record W2904049210 · doi:10.1111/1911-3838.12187

Financial Reporting Choices and Labor Contract Negotiations: A Case Study in the University Sector

2018· article· en· W2904049210 on OpenAlexaffvenueabout
Cameron K.J. Morrill, Janet Morrill, Gary Spraakman

Bibliographic record

VenueAccounting Perspectives · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsYork UniversityUniversity of Manitoba
Fundersnot available
KeywordsNegotiationCollective bargainingEarningsAccountingWageEconomicsBusinessFinanceLabour economicsPolitical science

Abstract

fetched live from OpenAlex

Abstract A sizeable literature has developed that considers the interest of labor unions in employer financial statements and the effect that interest has on employer accounting decisions. Empirical results have been mixed, but there is at least some evidence that employers facing union pressure engage in earnings management and strategic disclosure decisions that help to win concessions from labor unions. We use a model of labor negotiations (Walton et al., 2000) to focus on a single employer–union relationship, the University of Manitoba (UM) and its academic faculty union, the University of Manitoba Faculty Association (UMFA). UMFA performed and published an analysis of the UM's financial statements in preparation for its 2010 round of collective bargaining, allowing us to identify accounting variables key to that analysis. We show that UM deducted internal restrictions and capital transfers from operating income to give the impression that its ability to pay was compromised. In assessing 16 subsequent disclosure events by UM, its disclosure strategy appears to reflect primarily, but not uniformly, a forcing bargaining posture. Our analysis indicates that UM enjoyed considerable latitude in its financial reporting, which it used to its advantage in negotiations and to hinder budgetary oversight.

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)0.002
metaresearch head score (Gemma)0.021
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.105
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0000.000
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 head0.015
GPT teacher head0.246
Teacher spread0.231 · 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

Citations3
Published2018
Admission routes3
Has abstractyes

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