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Record W2009627522 · doi:10.1177/0170840606062430

Conventional versus Radical Moral Agents: An Exploratory Empirical Look at Weber’s Moral-points-of-view and Virtues

2006· article· en· W2009627522 on OpenAlexaff
Bruno Dyck, J. Mark Weber

Bibliographic record

VenueOrganization Studies · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicWeber, Simmel, Sociological Theory
Canadian institutionsUniversity of TorontoUniversity of Manitoba
Fundersnot available
KeywordsObedienceSociologyIndividualismEpistemologyMaterialismPositive economicsPsychologySocial psychologyLawPhilosophyEconomicsPolitical science

Abstract

fetched live from OpenAlex

Max Weber’s work suggests that conventional management theory and practice is underpinned by a moral-point-of-view that places relatively high emphasis on both materialism and individualism. He calls for the development of radical management theory and practices to serve as a counterpoint. Both the conventional and radical moral-points-of-view are associated with specific virtues and practices. Weber suggests that, from a conventional moral-point-of-view, four primary virtues—mercy, submission, obedience and non-worldliness—give rise to specialization, centralization, formalization and standardization. In contrast, from a radical moral-point-of-view, these same four primary virtues are expected to give rise to sensitization, dignification, participation and experimentation (Dyck and Schroeder 2005). Our study contrasts and compares a sample of conventional and radical managers, to provide an empirical look at these expected differences, as well as testing for differences in their personal and spiritual virtues. Implications are discussed.

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)0.010
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.026
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0030.009
Scholarly communication0.0030.007
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.151
GPT teacher head0.387
Teacher spread0.236 · 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 designQualitative
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

Citations35
Published2006
Admission routes1
Has abstractyes

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