The core of organisational reputation: taking multidimensionality, audience multiplicity, and agency subunits seriously
Bibliographic record
Abstract
Reputational theory holds that an agency’s reputation is a valuable political asset. However, since Daniel Carpenter’s influential contributions, reputational studies have focused on reactions to reputational threats, rather than on reputation as such. Therefore, there is a curious lacuna at the heart of reputation studies: A lot is known about reactions to reputational threats, but fundamental questions about reputation per se are left unexplored. This paper investigates the dimensionality of the reputational concept, its variation across types of audiences, and its consistency across agency subunits. This is done in the context of the EU Commission, the core executive institution of the European Union. While this case is interesting in itself, its main value is to suggest what a research agenda focusing on the core building blocks of reputational theory might look like. Our survey of actors in the EU’s Transparency Register provides mixed, but mostly supportive evidence.
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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 | 0.015 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.015 | 0.025 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".