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Record W2092761934 · doi:10.1108/09696470210442132

Rationalizing the promotion of non‐rational behaviors in organizations

2002· article· en· W2092761934 on OpenAlexaff
Peter Smith, Meenakshi Sharma

Bibliographic record

VenueThe Learning Organization · 2002
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicOrganizational Learning and Leadership
Canadian institutionsSmiths Detection (Canada)
Fundersnot available
KeywordsRationalityOpenness to experiencePsychodynamicsPsychologyLeverage (statistics)Promotion (chess)Emotional intelligenceAction (physics)Public relationsSocial psychologySociologyEpistemologyPolitical sciencePsychotherapistComputer science

Abstract

fetched live from OpenAlex

Contends that organizations designed according to current theories require that traits of leadership and personal responsibility be developed in employees at all levels of the organization, not just the formal leaders. Asserts that to develop these traits, organizations must strike an adequate balance between rationality/technical efficiency and non‐rational factors such as emotion. States that organizations currently operate with a facade of rationality, ignoring emotional reality. Argues that leverage for such change lies in working at team/group level meetings, changing the quality of interactions to enhance authenticity and create emotional openness. Maintains that action learning has so far proven the best vehicle for releasing emotional energy into the workplace if facilitators are utilized who can enrich the action learning process with skills drawn from disciplines such as counseling, Gestalt, psychodynamics, and psychoanalysis. Claims that familiarity with the principles of Eastern philosophies is also helpful.

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)0.009
metaresearch head score (Gemma)0.009
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 score0.009
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.017
Scholarly communication0.0050.003
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.024
GPT teacher head0.221
Teacher spread0.197 · 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

Citations24
Published2002
Admission routes1
Has abstractyes

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