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Record W3122557030

Justifying Accounting Change Through Global Discourses and Legitimation Strategies. The Case of the UK Central Government

2016· article· en· W3122557030 on OpenAlex
Noel Hyndman, Mariannunziata Liguori

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.

Bibliographic record

VenueDurham Research Online (Durham University) · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Policy and Administration Research
Canadian institutionsQueen's University
Fundersnot available
KeywordsLegitimationRationalisationAccountingManagement accountingPoliticsGovernment (linguistics)New public managementPolitical scienceRhetorical questionConstruct (python library)IdeologyPublic relationsBusinessPublic sectorLaw
DOInot available

Abstract

fetched live from OpenAlex

Accounting has been viewed, especially through the lens of the recent managerial reforms, as a neutral technology that, in the hands of rational managers, can support effective and efficient decision-making. However, the introduction of new accounting practices can be framed in a variety of ways, from value-neutral procedures to ideologically charged instruments. Focusing on financial accounting, budgeting and performance management changes in the UK central government, and through extensive textual analysis and interviews in three government departments, this paper investigates: how accounting changes are discussed and introduced at the political level through the use of global discourses; and what strategies organisational actors subsequently use to talk about and legitimate such discourses at different organisational levels. The results show that in political discussions there is a consistency between the discourses (largely New Public Management) and the accounting related changes that took place. The research suggests that a cocktail of legitimation strategies was used by organisational actors to construct a sense of the changes, with authorisation, often in combination with, at the very least, rationalisation strategies most widely utilised. While previous literature posits that different actors tend to use the same rhetorical sequences during periods of change, this study highlights differences at different organisational levels.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.941
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.002
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.165
GPT teacher head0.438
Teacher spread0.273 · 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