The Emergence of Mythologies in Organisations: A process model
Bibliographic record
Abstract
Research on organisational storytelling has shed light on different types of narratives. A specific story type, organisational myths, has caught the interest of some scholars in the field, but has not been theorised in any great detail. While it is rarely disputed that organisational members can and do develop emotional connections to myths and mythical stories in their social context, how and why these myths, or ‘sacred stories’, emerge in organisational settings has remained mostly unaddressed. Therefore, drawing upon a Jungian psychosocial approach, we propose a process model for the emergence of mythologised stories in organisations by situating members’ psychological dynamics within the social context in which they emerge. We propose that a conscious understanding of the conditions that lead to the emergence of mythologised stories in organisations can help clarify and deepen the relationship between different types of stories to support and sustain organisational change and development. The paper contributes to the existing literature on organisational storytelling and myths within organisational studies.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".