Controllers or catalysts for change and improvement: would the real value for money auditors please stand up?
Bibliographic record
Abstract
Public finance watchdogs and guardians of the public purse. These are just two of the labels branded on Auditors General over the years. Since their initiation into value for money audit only three decades ago, they have continually been exposing cases of public financing laxity. Each time the auditors approach a government organization, their reputation invariably precedes them. But what happens when the controllers decide to turn over a new leaf and become catalysts for change and improvement who have come to help managers improve their public organizations? Auditees, totally unaccustomed to seeking help from auditors, are understandably befuddled. On the other side of the coin, auditors are equally inexperienced at providing assistance to auditees. There is many a slip twixt cup and lip before the role confusion dissipates.
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 | 0.011 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.026 | 0.033 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".