Bibliographic record
Abstract
ABSTRACT In English‐speaking academe, philology has virtually disappeared as a defined discipline, although its traditional array of skills and techniques for reading, editing, and interpreting texts are indispensable to fields ranging from biblical studies through every language and literature and are central to historical research. Philology's status “now” seems to be that the analytic skills for dealing with texts, skills developed over centuries, have been appropriated by multiple academic specialties while the framework that used to contain them has been dismantled and nearly forgotten. From a historian's viewpoint, I track the lively resistance movement pressing for a return to philology and a turn to a “new” philology, a revived, recovered, restored discipline with its own coherent identity. This movement of renewal and restoration is complicated by the need to come to terms with philology's deep entanglement with racialist thought and anti‐Semitism, a past that has indelibly stained the reputation of philology as a discipline. The guiding intent of this article is to bring philology, with all its complications, back together with history, its formerly yoked companion discipline, and inquire where and how theory emerges—a metaphilology analogous to metahistory.
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.063 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".