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Record W4409959256 · doi:10.1111/1911-3846.13033

Translating, resisting, or escalating government programs? Accounting at the intersection of centrally imposed programs and local responses

2025· article· en· W4409959256 on OpenAlexfundvenueno aff
Ileana Steccolini, Carmela Barbera

Bibliographic record

VenueContemporary Accounting Research · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAccounting and Organizational Management
Canadian institutionsnot available
FundersQueen's UniversityQueen's University Belfast
KeywordsIntersection (aeronautics)Government (linguistics)BusinessAccountingPolitical scienceEngineeringTransport engineeringPhilosophy

Abstract

fetched live from OpenAlex

Abstract This article examines the role of accounting in the recursive processes of continuous adjustment to programs that emerge when programs are imposed by central government on local government. Focusing on the Italian context and adopting the conceptual lens of governmentality, our study contributes to the extant literature by highlighting the role of accounting in the power dynamics and transactional realities at the intersection between the governors and the governed. In doing so, it considers how accounting can shape plural local government conducts and counter‐conducts and how this, in turn, affects programs imposed centrally. It also sheds light on the transactional realities inherent in multiple, layered forms of central disciplining power and how this plays out to recursively redefine central discipline and local autonomy. The study highlights the importance of considering the different ways in which power is enacted and resisted through accounting in governmentality studies. By taking a pluralist and dynamic view of the ways in which programs are implemented, the study reveals multiple local translations and outcomes, as well as the underlying power dynamics at play.

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)0.007
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.493
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0020.001
Scholarly communication0.0010.001
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 head0.047
GPT teacher head0.301
Teacher spread0.254 · 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
Published2025
Admission routes2
Has abstractyes

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