Reputation management for regulatory agencies focused on government relations
Bibliographic record
Abstract
Reputation management is not straightforward and is an ongoing and active process. This paper considers the unique challenges to reputation management for regulatory agencies by considering five problems: politics, consistency, charisma, uniqueness, and excellence. This research advances the existing literature to consider how a regulatory agency can mitigate these problems in order to develop reputation management strategies with government decision-makers. Carpenter’s regulatory agency reputation management theory, the foundation of this research, emphasizes the fundamental importance of the legitimacy for regulators and categorizes regulatory reputation into four dimensions: performative, moral, technical, and legal procedural. Through a constructivist/interpretive paradigm viewpoint, data was gathered through six semi-structured interviews with current and former non-elected government Alberta Department of Energy civil servants. The Alberta Utilities Commission is referenced throughout the paper as an example that illustrates the unique challenges and how Carpenter’s theory can provide a strategic lens for regulators engaged in reputation management focused on government relations. The findings reveal that there is a fundamental importance that a regulator maintain legitimacy through a clear vision of its raison d’être. In addition, communication should be regular, proactive, transparent, and constructive to build relationships with people in all levels of government including elected officials that emphasizes how the agency is achieving policy outcomes. The results of this study contribute to the small body of literature that considers how regulatory agencies seek to establish a public image and communicate that image to influence government decision-makers without negatively affecting the independent and adjudicative function of quasi-judicial regulators.
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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 | 0.041 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.021 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".