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Record W4283016257 · doi:10.1111/faam.12336

Institutional embeddedness and the language of accountability: Evidence from 20 years of Canadian public audit reports

2022· article· en· W4283016257 on OpenAlexaffabout
Catherine Liston‐Heyes, Luc Juillet

Bibliographic record

VenueFinancial Accountability and Management · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Policy and Administration Research
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsAuditAccountingAccountabilityInternal auditMandateEmbeddednessContext (archaeology)Joint auditBusinessPerformance auditPublic relationsRhetorical questionEmotivePolitical scienceSociologyLaw

Abstract

fetched live from OpenAlex

Abstract Due to the expansion of the mandate assigned to public auditors in the past decades, audit reports have become more prominent indicators of the quality of government. Accordingly, it is important to investigate the factors that shape the communication of audit findings. We suggest that while internal and legislative auditors belong to the same community of practice, they are also embedded in distinct institutional environments that incentivize them to report their findings in different ways. In particular, we hypothesize that to draw attention and mobilize support for their work, legislative auditors are encouraged to use a language that is more negative and emotive than internal auditors. Applying methods of computational text analysis to a corpus of 3245 audit reports produced in the Government of Canada between 2000 and 2019, we present empirical evidence in favor of these hypotheses. Among other things, our findings provide large‐sample evidence that despite comparable professional norms and guidance, public auditors are sensitive to their institutional context and, in response to their environment, resort to rhetorical strategies to either amplify or mitigate the reputational risks associated with their reports.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.729
Threshold uncertainty score0.560

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
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 head0.051
GPT teacher head0.332
Teacher spread0.282 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations6
Published2022
Admission routes2
Has abstractyes

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