Accountability legislation: Implications for financial and performance reporting
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.044 | 0.099 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.020 | 0.020 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".