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Record W2565059119 · doi:10.22495/jgr_v1_i1_p3

Accountability legislation: Implications for financial and performance reporting

2012· article· en· W2565059119 on OpenAlexaboutno aff
Daphne Rixon

Bibliographic record

VenueJournal of Governance and Regulation · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAccounting and Organizational Management
Canadian institutionsnot available
Fundersnot available
KeywordsAccountabilityLegislationStakeholderAgency (philosophy)BusinessPublic administrationPublic sectorAccountingPublic relationsPolitical scienceLawSociology

Abstract

fetched live from OpenAlex

The purpose of this case study is to first examine the implications of accountability legislation on the financial and performance reporting of a public sector agency in the Canadian province of Newfoundland and Labrador and secondly, to compare the level of accountability with Stewart’s (1984) ladder of accountability. This paper is based on the first phase of a two-phase study. The first phase focuses on the initial impacts of accountability legislation on agencies and the challenges created by the legislation’s ‘one size fits all’ approach. The second phase of this study will examine the impact of the legislation on stakeholders after it has been in operation for five years. The second phase will include interviews with stakeholders to ascertain the level of satisfaction with the new legislation. The first phase of the study is significant since it highlights how governments could consider stakeholder needs when drafting such legislation. This research contributes to the body of literature on stakeholder accountability since there is a paucity of research focused specifically on the impact of accountability legislation on public sector agencies. An important contribution of this paper is the introduction of a framework for legislated accountability reporting. The main theoretical frameworks used to analyse the findings are Stewart’s (1984) ladder of accountability in conjunction with Friedman and Miles (2006) ladder of stakeholder management and engagement.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.244

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.021
GPT teacher head0.248
Teacher spread0.227 · 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 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
Published2012
Admission routes1
Has abstractyes

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