Bibliographic record
Abstract
The effectiveness of a well-told story cannot be gauged solely by such objective features as plot structure and character development; nor can its achievement be decided by the subjective responses of readers and listeners alone. The skill of a narrator lies both in representing actions and characters convincingly, and in anticipating their reception by readers and their impression on listeners. When the young Weber gave a lecture entitled ‘The Social Causes of the Decay of Ancient Civilization’ in Freiburg in 1896, he began by acknowledging that, although the familiar story (Geschichte) that he was recounting might have a certain appeal to the modern taste for tragedy; its scholarly merit as a history (Geschichte) must rest on its logical structure and empirical rigour. Since his educated listeners were presumably concerned with how modern world powers such as Germany might be defeated or emerge victorious in the struggle between nation-states, his account of the rise and fall of the empires of antiquity could be expected to evoke the sense that ‘this story is told about you’ (de te narratur fabula). But unlike Horace, whom Weber is quoting here, and who is warning readers of his Satires that his portrait of the greedy Tantalus would only require a ‘change in name’ (mutato nomine) to be an account of their own greedy ways, Weber establishes his scholarly good faith (bona fides) by disavowing any direct identification between the events of the past and the conditions of the present.
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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.043 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".