Narrative and the Construction of Myths in Organizations
Bibliographic record
Abstract
This chapter explores how organizational myths are created and sustained in organizational settings. Organizational, sociological, and anthropological literature on myth and mythologizing are reviewed to create a framework for the analysis of mythologizing in organizations. The chapter applies this framework, using a qualitative-interpretive perspective, to analyze the famous 2005 Stanford Commencement Speech by the late Steve Jobs, creator of Apple Computers, who was one of the most successful myth creators in the contemporary business world. His example of myth creation is vivid and allows for analysis of how myths are created and distributed in organizational settings. In the analysis, we identify and explore three domains or layers of complexity uncovered in the speech: (1) narrative and rhetorical devices used in the speech, (2) role and features of the monomyth or hero’s journey in the speech, and (3) mythological archetypes or “mythemes” that Jobs draws on to narrate his journey at Apple computer and in his broader life. The speech creates Jobs as a mythical hero and relates Jobs’s accomplishments to broader social myths. The chapter contributes to process organization studies by developing a process-oriented framework for understanding how myths are created and used to legitimate organizations.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.047 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".