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.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".