Building Public Sector Reputation: An Examination of Child Welfare in Ontario
Bibliographic record
Abstract
Building positive reputation in child welfare has been compared by professionals in the field to attempting to swim against the current. That the sector faces challenges in bolstering its public image is not surprising, as it is seen as a service of last resort for families experiencing significant challenges (Swift & Callahan, 2002). Existing research primarily examines how child welfare organizations attempt to manage reputation through media. With media being just one input that citizens consider when developing their perceptions of these highly complex agencies, there is a need for research that looks at reputation management holistically through models developed by scholars in the field (Fombrun & van Riel, 2007; Luoma-aho, 2008; Grunig & Hung-Baesecke, 2015). This study sought to understand how and to what extent Children’s Aid Societies (CAS) in Ontario manage their reputations by developing trust with citizens and ensuring positive outcomes for clients. The results demonstrate that, although the public supports the concept of child welfare, the work of these agencies remains mysterious to most. This presents an opportunity and need for CASs to take control of the narrative and tell their own stories to the community. Based on the findings of in-depth interviews with CAS professionals and a nonprobability survey of Ontario residents, this study proposes a Child Welfare Reputation Index. This tool can be used by CASs to assess their reputations and develop a roadmap to enhance standing within their communities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.006 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.057 | 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 teacher head, 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".