Bibliographic record
Abstract
Sentimental novels are cluttered with things.The emotional attachments that people form with possessions in these mid-eighteenth-century fictions can seem as freighted with consequence as the emotional attachments that people form with each other.Indeed, modem readers of Henry Brooke's The Fool of Quality or Laurence Sterne's A Sentimental Journey might be pardoned for finding it hard to distinguish one sort of relationship from the other-even if normal notions of the folly of fetishism predispose us to believe that the difference between, say, ownership and friendship is a difference worth preserving.The keepsakes that clutter sentimental fiction (the lockets that protagonists wear next to their hearts; the sleeve buttons or snuffboxes that pairs of characters exchange to memorialize their first meeting or last, teary-eyed parting) work instead to collapse that difference.While they instructed their readers in emotional responsiveness, sentimentalists were more than ready to make objects of this variety-objects particularly valued because they are the surrogates for particular persons-their props.This practice marks the novelists' fashion-consciousness.On the testimony of the OED, which dates the word keepsake to 1790, it was only in the eighteenth century that keepsakes came to be identified as a distinct kind of material good.The fact that by 1 790 members of the propertied classes had learned to want to give and to receive keepsakes from one another bespeaks the reciprocal influence
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 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".