From an association of individuals to communities of persons: how to foster complexity to understand diversity in organizations
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.010 | 0.045 |
| Scholarly communication | 0.012 | 0.025 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".