Bibliographic record
Abstract
WRITING TIME I am now & then haunted by some semi mystic very profound life of a woman, which shall all be told on one occasion; & time shall be utterly obliterated; future shall somehow blossom out of the past. One incident – say the fall of a flower – might contain it. My theory being that the actual event practically does not exist – nor time either. Virginia Woolf, Diary , 23 November 1926 This notion of Time embodied, of years past but not separated from us, it was now my intention to emphasize as strongly as possible in my work. Marcel Proust, Time Regained To write of memory, time, and desire in early twentieth-century literature is to touch the place where modernism's intense concerns with its historicity and belatedness converge with the versions of temporalities and sexualities it was articulating; it is to investigate the sustained provocation of a modernist predisposition to think of the past through the language of sensuality and eros. T. S. Eliot's now well-known lines from the opening of The Waste Land , “April is the cruelest month, breeding / Lilacs out of the dead land, mixing / Memory and desire, stirring / Dull roots with spring rain,” capture an agonizingly raw protestation within the modernist project, offering one of those rare moments when a poetic conceit happens to express a key dilemma of the time.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.524 | 0.338 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".