MétaCan
Menu
Back to cohort
Record W2119792744 · doi:10.1506/ap.6.3.5

Improving Interim Reporting/L'Amélioration de L'Information Financière Intermédiaire

2007· article· en· W2119792744 on OpenAlexaffvenueabout
Christine I. Wiedman

Bibliographic record

VenueAccounting Perspectives · 2007
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPolitical scienceHumanitiesInterimLawArt

Abstract

fetched live from OpenAlex

ABSTRACT This paper is based on the Interim Reporting session of the Maintaining Quality Capital Markets through Quality Information Conference. Several significant initiatives relating to interim reporting were introduced and debated, including moving to a mandatory review regime in Canada and requiring firms to issue a review engagement report on the interim financial statements to the public. Conference participants were asked to vote on seven questions relating to these initiatives. This paper summarizes the results of the voting and discussion from the conference. Related academic research is also presented. RÉSUMÉ Le présent article s'inspire de la séance du congrès sur le rôle de la qualité de l'information dans le maintien de la qualité des marchés financiers ayant porté sur l'information financière intermédiaire. Plusieurs projets importants relatifs à l'information financière intermédiaire ont été présentés et débattus, notamment la perspective du passage à un régime d'examen obligatoire au Canada et de l'obligation pour les entreprises de produire un rapport de mission d'examen des états financiers intermédiaires à l'intention du public. Les participants au congrès ont été appelés à voter sur sept questions relatives à ces projets. Le présent article résume les résultats du vote et les discussions tenues dans le cadre du congrès. Des recherches universitaires connexes sont également présentées.

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)0.003
metaresearch head score (Gemma)0.029
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.706
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.029
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.007
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 head0.009
GPT teacher head0.244
Teacher spread0.234 · 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

Citations2
Published2007
Admission routes3
Has abstractyes

Explore more

Same venueAccounting PerspectivesSame topicAuditing, Earnings Management, GovernanceFrench-language works237,207