Bibliographic record
Abstract
We leave the prints of our body, the touch of flesh on metal and stone. We constantly wear things out, with our hands, our feet, our backs, our lips. And we leave the traces of singular actions: the unintentional. The random, the intimate, unplanned touch on history’s passing: we break twigs, move pebbles, crush ants … all the signs that trackers learn to read. We leave footprints, as Neil Armstrong did on the Moon. Introduction: ex-cavate [to uncover or lay bare by digging; to unearth] Like most discussions involving the palimpsest this, too, begins with the concept’s common dictionary definition, ‘a parchment or other writing surface on which the original text has been effaced or partially erased, and then overwritten by another’. Palimpsesting originally referred to the technique used by ancient artisans to reuse scarce material for their inscriptions of new ideas and ideals of new, emerging worlds. In the word’s extended meaning, a palimpsest is more generally described as ‘a thing likened to such a writing surface, esp. in having been reused or altered while still retaining traces of its earlier form; a multilayered record’. The term has thus become a powerful metaphor for what Freud described as the ‘receptive surface … legible in suitable lights’, any surface, really, onto which New superimposes itself on Old. Samuel Coleridge is generally credited with introducing the palimpsest as a literary metaphor, but it was Thomas de Quincey who wove it into a treatise on human memory: ‘What else than a natural and mighty palimpsest is the human brain?’ – anticipating, of course, the idea of the receptive surface. In a slightly different vein Rudolph Byrd evokes Stuart Hall’s notion of metaphors of transformation, suggesting that the palimpsest may indeed function as a master metaphor in this respect, challenging us to ‘think expansively beyond the boundaries of what is known about the relations between the social and the symbolic‘.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".