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Record W2339860502 · doi:10.1007/s10551-016-3150-6

Exploring the Diversity of Virtues Through the Lens of Moral Imagination: A Qualitative Inquiry into Organizational Virtues in the Turkish Context

2016· article· en· W2339860502 on OpenAlexaff
Fahri Karakaş, Emine Sarigöllü, Selçuk Uygur

Bibliographic record

VenueJournal of Business Ethics · 2016
Typearticle
Languageen
FieldDecision Sciences
TopicEthics in Business and Education
Canadian institutionsMcGill University
Fundersnot available
KeywordsBusiness ethicsSociologyVirtue ethicsTurkishCapitalismMoral disengagementEpistemologyVirtueEnvironmental ethicsLawPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

The purpose of this article is to introduce a multidimensional framework based on the concept of moral imagination for analysing and capturing diverse virtues in contemporary Turkish organizations. Based on qualitative interviews with 58 managers in Turkey, this article develops an inventory of Turkish organizational virtues each of which can be associated with a different form of virtuous organizing. The inventory consists of nine forms of moral imagination, which map the multitude of virtues and moral emotions in organizations. Nine emergent forms of moral imagination are based on: integrity, affection, diligence, inspiration, wisdom, trust, gratefulness, justice, and harmony. The findings have made a contribution to the expanding literature on how Islamic organizations develop their business ethics through a repertoire of virtues. An empirical account of the range of virtues in organizational contexts that have emerged as a result of the hybridization of Islamic virtue/aesthetics and neoliberal capitalism in contemporary Turkey is provided. A theoretical contribution is made to business ethics literature through a phenomenology of virtues that provides unique insights on diverse forms of moral imagination in contemporary Turkey where Islam and neoliberal capitalism dynamically co-exist.

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 imitation

Not 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.

metaresearch head score (Codex)0.018
metaresearch head score (Gemma)0.038
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.541
Threshold uncertainty score0.970

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.038
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.002
Scholarly communication0.0000.002
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.698
GPT teacher head0.473
Teacher spread0.225 · 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 teacher head, not a consensus.

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

Citations24
Published2016
Admission routes1
Has abstractyes

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