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Record W2114229465 · doi:10.1177/0275074009360372

Monastic Governance: Forgotten Prospects for Public Institutions

2010· article· en· W2114229465 on OpenAlexaboutno aff
Emil Inauen, Katja Rost, Bruno S. Frey, Fabian Homberg, Margit Osterloh

Bibliographic record

VenueThe American Review of Public Administration · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicLocal Government Finance and Decentralization
Canadian institutionsnot available
Fundersnot available
KeywordsCorporate governanceIncentiveAgency (philosophy)Quarter (Canadian coin)Public sectorGermanPublic administrationPerspective (graphical)Principal–agent problemPolitical sciencePublic relationsEconomicsSociologyLawSocial scienceManagementMarket economy

Abstract

fetched live from OpenAlex

To overcome agency problems, public sector reforms started to introduce businesslike incentive structures to motivate public officials. By neglecting internal behavioral incentives, however, these reforms often do not reach their stated goals. This research analyzes the governance structure of Benedictine monasteries to gain new insights into solving agency problems in public institutions. A comparison is useful because members of both organizational forms, public organizations and monasteries, see themselves as responsible participants in their community and claim to serve the public good. This research studies monastic governance from an economic perspective. Benedictine monasteries in Baden-Württemberg, Bavaria, and German-speaking Switzerland have an average lifetime of almost 500 years, and only a quarter of them broke up because of agency problems. The authors argue that they were able to survive for centuries because of an appropriate governance structure, relying strongly on the intrinsic motivation of the members and internal control mechanisms. This governance approach differs in several aspects from current public sector reforms.

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 imitation

Not 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.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.020
Scholarly communication0.0080.011
Open science0.0010.005
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.051
GPT teacher head0.365
Teacher spread0.314 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations27
Published2010
Admission routes1
Has abstractyes

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