A Fool's Journey. The Promise and Peril of Storytelling for Organizations: A Settler's Meta Narrative
Bibliographic record
Abstract
My intention was to consider the literature and practices around institutional storytelling in an effort to learn how to best harness the power of narrative in an organizational environment. \nAn analysis of the literature around organizational and business storytelling trumpeted the trend as a kind of sense-making compass. Yet embedded in many of these texts is a sense that the transformative and emancipatory power of storytelling has been beggared in this environment, reduced to a humble servant of profit and power. \nVia a literature review as well as case studies of Kaapittiaq, Tim Hortons, the narrative of Canada, and reflective storytelling, I became convinced of the necessity of narrative deconstruction, arguing for a plurivocal practice in which storytelling is an instrument of discovery and self-reflection rather than that which produces a static product for commercial purposes. \nIn keeping with the ethos of the research, I chose to structure this paper in something akin to a narrative style, rather than a conventionally academic one. The intention is to bring the reader on a narrative (and cumulative) journey of discovery, building to the presentation of a storytelling tool at the end of the paper.
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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.026 |
| Scholarly communication | 0.021 | 0.020 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".