Fiction And Fission: Twentieth — Century Writing on The Founding Fathers Fiction And Fission: Twentieth — Century Writing on The Founding Fathers
Bibliographic record
Abstract
In Australia, tribesmen trace their origins to a rain-making python who drowned a set of incestuous sisters because they polluted his watering place with their menstrual blood) In Tibet, families derive their descent from a beautiful boy in a conch .egg which came from the immense ovum exuded from the essence of the five primordial elements. 2 In Polynesia, islanders tell tales of the emergence of their clan forebears from holes in the ground.' And in every other quarter of the globe, other people proclaim other origin myths which are beyond reasoning but not without reasons. In Australia, tribesmen trace their origins to a rain-making python who drowned a set of incestuous sisters because they polluted his watering place with their menstrual blood) In Tibet, families derive their descent from a beautiful boy in a conch .egg which came from the immense ovum exuded from the essence of the five primordial elements. 2 In Polynesia, islanders tell tales of the emergence of their clan forebears from holes in the ground.' And in every other quarter of the globe, other people proclaim other origin myths which are beyond reasoning but not without reasons.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.020 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".