Bibliographic record
Abstract
Abstract “Show, don't tell” is a maxim basic to literary craft. It enjoins avoidance of abstract, cliché‐ridden summaries and use of rich, vividly rendered details. Anyone who has attended an introductory creative writing course will have encountered it. Practised literary writers know it is true. Why is showing so fundamental to good literature? Why is it more effective than telling? Showing constellates details, placing facets of a larger shape before the reader's mind, a shape that cannot be adequately encompassed by a summary, whose power lies in the simultaneous integration of multiple, superficially discontinuous aspects. This sounds like the eureka effect, the “Aha!” of sudden discovery in mathematics and the sciences. I argue that grasping what is being shown in a literary context is indeed related to insight in theoretical fields. Telling the reader “what happened” makes the mind's eye glaze over in just the way that it glazes over when it is forced to memorize formulae that it does not understand. Showing is like offering an elegant proof; the mind reaches to understand what is going on. When it succeeds, it feels the satisfaction of having grasped meaning.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.235 | 0.102 |
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".