The Impact of Ombudsman Investigations on Public Administration: A Case Study and an Evaluation Guide
Bibliographic record
Abstract
[Executive Summary]: "It is not easy to evaluate the impact ombudsman have on the operations of a government or organization. While there are clear benefits for residents who have their problems solved, what are the benefits for the day-to-day operations and processes of a public service? It is difficult to point to the money saved and efficiencies found by ombudsman work. A comprehensive review of English-language literature on the subject of evaluating ombudsman impact turned up very little. That is because the ombudsman’s work focuses on something that is inherently difficult to measure: fairness in the way that government treats its citizens. This study breaks new ground by establishing how the Toronto Ombudsman’s office has, in the past five years, led to a more efficient and responsive city administration. Part I of this innovative project is an independent, in-depth, interview-based case study of the observed impacts of ombudsman investigations in the Toronto Public Service. Investigations are at the centre of ombudsman work: they involve complex and conflicting information, in-depth analytical work, and issues that often generate public interest and media attention. The investigations are frequently systemic or system-wide, allowing ombudsman to have a meaningful impact on many people at once. This report provides ombudsman with a set of tools that can be used to evaluate the impact of their work. Part research report and part evaluation guide, this publication leads practitioners through an evaluation process with a particular focus on the impact of ombudsman investigations on public administration. This has been a collaborative effort between researchers from Ryerson University and the Toronto Ombudsman’s office. It was funded with the help of a generous contribution from the International Ombudsman Institute. The work would not have been possible without the advice and guidance of an advisory group consisting of experts in the field from across North America."
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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 | 0.052 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".