Bibliographic record
Abstract
At the outset there is need to clarify usage of the term “Ombudsman”. Statutes and other official references usually pertain to the position of the Ombudsman, while common usage may either be to the position or to the current incumbent who occupies that position. It is thus necessary to be cognizant of the context in which the term is used. We will often use the term “OmbudsOffice” where appropriate because the staff in the Ombudsman’s office usually plays a key role in processing and deciding public complaints. The Classical Model To revisit an old idea, Parliament’s task is not to govern but to make certain that Government does govern in the public interest, fairly, and impartially according to law. This task includes the monitoring of administrative decision-making – a task that has become more difficult with the growth of the welfare state following World War II, as more administrative decisions of an increasingly complex and technical nature were implemented by specialists in the public bureaucracy. Not surprisingly, with this growth of the modern administrative state, there was an accompanying increase in complaints of alleged administrative wrong-doings with no efficient means for their resolution. It was in this context that Sir Guy Powles, New Zealand’s first Ombudsman, expounded for his Canadian audience in the mid-1960s how and
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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 | 0.018 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.020 | 0.009 |
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".