Threats to the New Zealand Serious Fraud Office: an institutional perspective
Bibliographic record
Abstract
Purpose The purpose of this paper is to evaluate threats to dissolve the New Zealand Serious Fraud Office (SFO) as interpreted through the public press. Design/methodology/approach An institutional approach is adopted in this case, and the analysis is driven by Oliver's understandings of antecedents to deinstitutionalization. Relevant press articles are reviewed, and SFO history and New Zealand socio‐political context inform the analysis. Findings The paper identifies over 1,800 articles (September 2003 to October 2008) and analyses the content of those 157 that contain views on the SFO itself. This analysis reveals that while there is a strong political antecedent to the proposed change, the media is dominated by weakly evidenced but emotive functional and social arguments. The susceptibility of the SFO to political influence, and a less‐than‐fully engaged media, is shown to provide a risk of deinstitutionalization to this politically dependent office. Research limitations/implications Conclusions suggest how a relatively new and possibly politically naïve organisation may be, by necessity, starting to come to terms with its own external dependencies. Social implications The SFO may be evolving new relational norms in response to its own vulnerabilities in a political environment. There may be lessons for others in this analysis of a norming process, and further research into such processes would be a rich area for further study. Originality/value The contribution is in forming an understanding of the media patterns and in analysing what they convey as to the threatened deinstituitonalization of the SFO.
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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 | 0.014 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.009 | 0.039 |
| Scholarly communication | 0.027 | 0.013 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".