Bibliographic record
Abstract
Abstract Administrative culture is an established and prominent theme in public administration research. It is frequently used to explain or contextualize a variety of phenomena in the discipline, ranging from differences in governing styles and policy outcomes between national bureaucracies to making sense of the informal norms and values that determine the activities of individual public organizations and how they interact with political and non-state interests. It is also occasionally used to characterize a particular “type” of organizational culture, with features that distinguish it from the private or third sectors. With such varied uses of the term, as well as related concepts such as administrative style, tradition, and legacies, administrative culture attracts multiple interpretations as well as its fair share of criticisms as an explanatory tool. In some contexts, administrative culture is an independent variable that helps explain divergence and variety in policy outcomes within and across national borders, while in others it is a dependent variable that attracts experiments and new measurement tools with the aim of producing more sophisticated understanding of its place in public governance. Early skepticism about the study of administrative culture mainly arose due to the absence of adequate methodology as well as uncertainty about how to begin empirical research into the concept. The emergence of such a methodology and tools for inquiry since the 1970s has meant that administrative culture is now firmly located in the literature and practice of government and a burgeoning literature now exists across the globe. Some of the key contemporary debates around administrative culture concern the interplay between cultures and sub-cultures within bureaucracies, the influence of distinctive administrative traditions and styles on policy outcomes, and the role culture plays in public sector reform.
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 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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.116 | 0.058 |
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".