Bibliographic record
Abstract
Noah Heringman’s important new book is surprisingly equivocal about its object of study. ‘Deep time’ is a metaphor, as vividly illustrated in a passage from the afterword where Heringman uses a figure to illustrate how far away human prehistory is for an observer ‘on the floor of the Grand Canyon’, looking up at the uppermost rim, where the 270-million-year-old strata still predate the first mammals (p. 229). Deep time figures extension in time as extension in space, a metaphor that’s naturalized in the science of geostratigraphy. Elsewhere in the afterword, Heringman refers to the idiom of deep time (pp. 235, 237, 238) as one that continues to shape fiction and science writing in the present. But earlier in the book, deep time is termed as a concept; it’s no criticism that readers are likely to finish the book with a less settled view of the supposed concept than they had at the outset. As Heringman relates, the term was popularized by Stephen Jay Gould in his 1987 book Time’s Arrow, Time’s Cycle; in Gould’s work, the concept of deep time derives from the geology of Charles Lyell and his precursor James Hutton and, from the evolutionary theory, Charles Darwin developed under Lyell’s influence. Lyell and Darwin produced theories by which great changes, in geology and in the morphology of living things, are effected by the gradual operation of constant forces over immense periods of time. These changes occur too slowly to be visible in an individual lifetime or even in the entire span of human history. Both Lyell and Darwin compare theories of life and the Earth that use shorter time frames to romances because they depend on rapid—often miraculous—transformations; what we now call deep time, they assert, is comprehensible by reason although it staggers the imagination.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.370 | 0.215 |
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".