Uncovering system teleology: a case for reading unconscious patterns of purposive intent in organizations
Bibliographic record
Abstract
Abstract Contemporary organizations are teleological—purposive—structures, designed to fulfil myriad societal needs. The purposive efforts of any organization are shaped by knowledge. Organizational knowledge includes both conscious and unconscious dimensions. This paper argues that a similar duality applies to organizational teleology. Organizational behaviour unfolds in service to consciously understood teleological aims (such as corporate strategies and business plans) and also unconscious teleological aims (that are undesigned or emergent), which are subtler to detect. Said differently, organizational behaviour is always purposive. Many of the intentions driving organizational behaviour are publicly understood and sanctioned; others are less well understood and unsanctioned. To the degree that some purposive behaviour in organizations remains unconscious, it may detract resources from managerial objectives and confound organizational change efforts. Drawing from facets of systems theory, this paper briefly discusses collective, purposive, and patterned characteristics of unconscious behaviour that may help practitioners to detect and respond to it. Copyright © 2003 John Wiley & Sons, Ltd.
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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 | 0.021 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.005 | 0.081 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".