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Record W4225158227 · doi:10.1108/jocm-01-2021-0026

From an association of individuals to communities of persons: how to foster complexity to understand diversity in organizations

2022· article· en· W4225158227 on OpenAlexaff
Marie-Noëlle Albert, Nadia Lazzari Dodeler

Bibliographic record

VenueJournal of Organizational Change Management · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsUniversité du Québec à Rimouski
Fundersnot available
KeywordsDiversity (politics)Association (psychology)Value (mathematics)OriginalityPsychologyDiversity managementSocial psychologySociologyKnowledge managementComputer scienceCreativity

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to propose to move from the organization as an association of individuals to communities of persons. Design/methodology/approach This is primarily a conceptual paper. However, it nevertheless underlies very practical aspects. Findings An organization should recognize each person within it as a human whom we must take the time to know, and with whom we must interact sincerely. One that only focuses on performance-related goals would not perform well. Indeed, it would increase situations that would generate significant stress and therefore significant costs. To conceive of the generalized complexity of persons makes it possible to manage with the paradoxes and the uncertainties related to the human species, in all conscience. Thus, it is possible to move from diversity management to a management for diversity, where we recognize the contribution of the differences of each person to the organization and where everyone can influence the other. Originality/value This paper emphasizes theories and practices that seem non-efficient whereas it is the contrary.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.087
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.097
GPT teacher head0.256
Teacher spread0.159 · 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 designObservational
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

Citations6
Published2022
Admission routes1
Has abstractyes

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