Bibliographic record
Abstract
Abstract The consolations of tragedy are dark, but their darkness is what rings true to readers whose lives already share it. In King Lear an enraged old man upends the lives of those around him. It’s a story of loss, forgiveness, and deeper loss. To show how this story can console, Arthur Frank draws upon both the decades he has spent witnessing serious illness and his own experiences of ageing. His reading presents King Lear as a resource for people living lives that are troubled, exemplifying how to find consolation in literature. Shakespeare did not write self-help books, but his plays can help: not by fixing but by making liveable what cannot be fixed. Shakespeare’s Dark Consolations invites readers, including those not already familiar with King Lear, to hear how the play’s words can speak for us when our own words fail, and how its characters can speak to us, becoming our companions. Frank understands tragedy as a form of human relationship: a tragic sharing. Cordelia’s words, ‘We are not the first / Who with the best meaning have incurred the worst’, express the companionship that makes vulnerability liveable. Shakespeare’s Dark Consolations is a companion to those who need the consolation that King Lear can offer.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.163 | 0.049 |
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".