Bibliographic record
Abstract
When the matter is diverse and confused, how should it otherwise be but that the species should be diverse and confused? 1 Robert Burton's reflection on the myriad species of melancholy provides a fitting introduction to this volume of the MHRA Working Papers in the Humanities.The following collection of articles comprises critical insights into the persistent theme of melancholy in its range of manifestations, a range which is reflected by the variety of terms invoked in describing this mental state through the ages, and across the disciplines of literature and science.For instance, Owen Holland's thought-provoking analysis of the 'mulleygrubs', which stubbornly persist in William Morris's utopian landscapes, reveals that even specific individual terms for melancholy are open to more than one interpretation, and may be appropriated, and distorted, for ideological purposes.Esra Almas, meanwhile, demonstrates a culturally specific construction of melancholy in a discussion of the Turkish term hüzün in Orhan Pamuk's Istanbul: Şehir ve Hatıralar (Istanbul: Memories and the City) (2003).With melancholy thus emerging as a nebulous concept which resists easy classification, the six articles featured engage with it under a variety of different guises, taking in issues of mourning, loss, love-melancholy, elegiac poetry and melancholy landscapes and moods.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.026 | 0.007 |
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".