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Record W4318486549 · doi:10.32920/21977006.v1

The Impact of Ombudsman Investigations on Public Administration: A Case Study and an Evaluation Guide

2023· preprint· en· W4318486549 on OpenAlexaffabout
Myer Siemiatycki, Andie Noack, J. Houghton Kane, Marc Yvan Valade, Fiona Crean, April Lim, Graeme Cook

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicOmbudsman and Human Rights
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsWork (physics)Government (linguistics)Administration (probate law)Public administrationService (business)Political sciencePublic servicePublic relationsBusinessLawEngineeringMarketing

Abstract

fetched live from OpenAlex

[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."

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 imitation

Not 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.

metaresearch head score (Codex)0.052
metaresearch head score (Gemma)0.042
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.277

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0520.042
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.009
Science and technology studies0.0120.005
Scholarly communication0.0080.006
Open science0.0040.006
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0090.001

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.

Opus teacher head0.304
GPT teacher head0.501
Teacher spread0.198 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2023
Admission routes2
Has abstractyes

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