Bibliographic record
Abstract
No EPIC is complete without a post-conference excursion.It has become such a mainstay of the biannual Ezra Pound International Conference that it's easy to forget how rare an event it is among academic gatherings.Perhaps above all, the excursion is a testament to the collegial atmosphere of this community of Pound scholars, a community that is as international as it is intergenerational.After several days of talks, panels, poetry readings, concerts, dinners, and late-night impromptu gatherings it is a wondrous thing that no one ever seems to grow tired of each other.Indeed, we all seem to want to more of it-hence the excursion!It should be said that none of this would be possible without the extraordinary commitment of the organizers.The 2019 excursion was no different, and any account of the trip worth its salt must acknowledge the hands-on management and leadership of Viorica Patea and John Gery.The EPIC in Salamanca was all the more special, as this was the first time it was held in Spain, a seminal country for Pound and an indelible experience for all of us.Burgos, the historic old capital of the northern kingdom of Castile, proved to be an ideal locale for our first stop.1* I owe a debt of gratitude to John Gery for several details featured in this account.The report also appears in Make It New.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.052 | 0.010 |
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".