Bibliographic record
Abstract
Narrative or the use of stories is an ancient discipline. Our ancestors evolved the ability to see the world through a set of abstractions, and thereby enabled the development of sophisticated language and the ability to use stories as a primary mechanism for knowledge transfer. The oral-history tradition was the only method of knowledge transfer for many eons and persists into the current day despite the prevalence of the written word. First Nation elders in Canada passing on their wisdom to young people facing the conflicts of old and new, a Seanachie (the Irish word that means far more than storyteller) ensconced with an enraptured audience around a peat fire, the Liars bench of the Midwest in the USA where old timers sit to swap tall tales, and the ubiquitous watercooler conversations of the modern organisation: all evidence the persistence of story. The archetypal story form of the myths of the Greek gods and the trickster stories of Native Americans find modern expression and use in Dilbert cartoons, and the old fairy stories of Europe find new expression in Hollywood. Good teachers always tell stories to provide context and life to otherwise dull material. Anyone joining an organisation will take months or years to hear and reexpress the key stories of past success and failure that form a key part of the organisation’s deep culture. Executives who abandon the tyranny of PowerPoint and instead tell a story rooted in their own experience nearly always discover the power of story to move people; to quote Steve Denning (2000), one of the early pioneers with his work in the World Bank—“Nothing else would do.”
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.223 | 0.083 |
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".