Bibliographic record
Abstract
Pathologizing love is not new to this age of modern psychology and self-help books. Indeed, much was written about ‘lovesickness’ as a serious medical condition in the early modern period and many cures were suggested for those afflicted with this potentially deadly disease, which is described not only as a physical illness, but also as a disorder of the mind or imagination that afflicts the soul.1 Yet, one of the most curious aspects of European lovesickness is that it is subtly fostered, if not promoted, by the very texts that decry and pathologize it. As Wack explains: The growing body of medical discourse on love made it possible for the literary representations of erotic passion to be interpreted mimetically or realistically, as reflections of real life. The cultural authority of medicine may have in part enabled the poetic fantasies of the troubadours to become the social realities of the late Middle Ages and early modernity.’2 The discourses providing descriptions of causes and cures for the disease can be said to be its transmitters, the vehicles through which it spread like wildfire across Europe throughout the early modern period, creating and fanning the flames of this contagion. The creation and sanctioning of love as a disease — through the ambivalent language of medical, philosophical, religious and literary discourses — thus makes it possible for individuals not only to identify with the discursive models but also to fashion themselves and their behaviours after texts and, ultimately, to shape new realities: life imitates art. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".