Bibliographic record
Abstract
If the sense of smell appears to have been eclipsed by the other senses in western culture, there is one realm in which it retains an almost mythic power — memory. reminiSCENT, a performance series curated by myself and Paul Couillard,1 acknowledged the powerful relationship between smell and memory and explored the artistic and cultural potential of this undervalued sense. Documentation in literature and science indicate that no other sense evokes memory as intensely as smell. For Marcel Proust, a whiff of a madeleine conjured up the world of childhood; for Helen Keller, smell was a “potent wizard” (181) that transported one across thousands of miles. Even newborns, after just a few days, recognize and remember their mothers via the distinctiveness of smell. As compelling as these olfactory experiences are, there is a tendency to regard smells purely on the level of immediacy. Yet fragrances also have complex social meanings. How, what and why we smell are subject to many cultural influences. One only has to consider hygiene and the myriad ways in which the body is bathed, cared for and scented to appreciate the role that smell plays in transmitting culture. In short, smell is as much a learned, cultural practice as it is a physical act of perception (see Drobnick Smell Culture).
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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".