Bibliographic record
Abstract
By the late Middle Ages, the language of the Anglo-Saxons was a dead language, presenting insuperable difficulties of comprehension and scorned in centres that owned copies of Old English texts. That Old English is a subject of study today is the fruit of the pioneering labours of generations of scholars who, beginning in the second quarter of the sixteenth century, undertook the task of recovering the knowledge of the language and bringing into the public domain texts written in it. The achievements of the first generations were substantial: they rescued from loss or destruction the Anglo-Saxon manuscripts upon which all subsequent study has ultimately depended; their transcriptions have in some cases preserved for posterity texts whose originals subsequently perished; they issued the earliest editions of Old English texts, and furnished the tools on which scientific study of the language could be based; and their efforts led to the eventual inclusion of Old English within the university curriculum. The purposes that impelled these early Anglo-Saxonists varied in character from antiquarian-historical to religio-political to, eventually, the more purely philological. This chapter will assess the nature and describe the progress of their work from the sixteenth-century beginnings up until the eighteenth century.
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 | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".