Preface and Acknowledgments
Bibliographic record
Abstract
Every perfume tells a story.What initially motivated my research for this book was the desire to better understand the stories of fragrance, suggested or elaborated, that waft from the pages of nineteenth-century books, to observe how these aromas mingle with plots and poetics, and to understand how readers of the era would have sensed and interpreted them.The perfumes of now classic works like Flaubert's Madame Bovary and Baudelaire's "Correspondances" reveal facets of form and meaning that were always there, though I had passed them by, again and again, before I began following literary scent trails.I first encountered Baudelaire's influential "Correspondances," a sonnet built on the lush notes of musk, amber, benzoin, and incense, in an undergraduate French literature course.As we analysed the poem line by line, a classmate asked what benzoin (benjoin) smelled like.The query seemed unusual in the critical context of a linguistic turn that shaped our discussions at the time, an approach that did not readily invite sensorial connections to the words we read.The reply, offered by a diligent student who had taken the time to investigate, was that it smells like tar.No one expressed surprise.I hardly gave it a thought.After all, Baudelaire mentions goudron alongside musc and huile de coco in the verse poem "La Chevelure" and its prose cousin, "Un Hémisphère dans une chevelure."But what if benzoin does not smell like tar? How would knowing the smell of benzoin influence the experience of reading Baudelaire's poem, or the direction of scholarly inquiry?As I learned much later in my olfactory fieldwork for this book, benzoin smells to me nothing like tar.I suspect the earlier misidentification was caused by a dictionary consultation that led to an inadvertent correspondance: a confusion of benzene (a hydrocarbon found in coal tar) and the sticky styrax benzoin, a tree ooze recommended in nineteenthcentury perfume manuals (along with ambergris, tolu, storax, and other
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.336 | 0.206 |
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".