Folger Shakespeare Library, creator; Rebeca Sheir, Neva Grant, and Barbara Bogaev, hosts. Shakespeare Unlimited
Bibliographic record
Abstract
The Folger Shakespeare Library's podcast, Shakespeare Unlimited, lives up to its name.With more than 200 episodes as of the writing of this review, this is truly a Shakespeare podcast without limitations.Episode topics run the gamut from the copy of Shakespeare's complete works making the rounds of the Robben Island prison during Nelson Mandela's incarceration there (episode 1) to an examination of Geoffrey Chaucer's Wife of Bath (episode 206), and everything in between.There are conversations with some of the great Shakespearean actors of our day, such as Ian McKellen (episodes 194 and 195); episodes that offer practical advice about staging Shakespeare's plays; episodes on Shakespeare and race; and episodes on comedy, history, tragedy, romance, the plague, adaptation, the American presidency, Star Wars, poetry, education and pedagogy, music, geography, and much more.Shakespeare experts and novices alike will be hardpressed to find nothing of interest here, as the podcast is a treasure trove of new ideas, new ways of thinking, and new avenues of exploration for Shakespeare and early modern England more generally.Episodes are pitched for a general audience and run around 30-40 minutes each.Listeners need not have any special affinity for or understanding of Shakespeare or his works to engage with these episodes.The hosts (Rebeca Sheir, Neva Grant, and Barbara Bogaev) do an excellent job of asking questions and helping situate the listener within each episode.Episode 200, for example, is a conversation between host Barbara Bogaev and scholar Ian Smith about his new book, Black Shakespeare: Reading and Misreading Race (2022).At Bogaev's prompting, Smith begins by debunking the perennial claim that there were no Black people in Shakespeare's England, and questions the ways that Shakespeare's contemporaries linked Black skin to evil.Smith then suggests that Shakespeare actually presents Othello as an analogue to Jesus in order to flip the "Black = bad" narrative on its head.While longtime podcast listeners (as well as Shakespeare scholars and practitioners) are likely to be familiar with the early modern practice of casting Black-skinned characters in the roles of
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Insufficient payload (model declined to judge) Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.326 | 0.238 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".