Bibliographic record
Abstract
A quotations dictionary on historical principles, the Dictionary of American Regional English (familiarly, DARE) owes much of its overall design and particular technique to the Oxford English Dictionary (OED), as DARE’s first chief editor, the late Frederic G. Cassidy, freely admitted. DARE is nonetheless an innovative dictionary, a bold synthesis of linguistic geography and historical lexicography. It developed new features of entry structure (maps, questionnaire responses, encyclopedic quotations, and others) in order to satisfy constraints specific to the lexicon (or perhaps to the type of lexicon) it describes, but also to test the limits and capacities of lexicographical technique conventionally understood, to alter somewhat the uses of historical lexicography, and to support new expectations of historical dictionaries from its quite diverse audiences. 1 A dictionary of “dialect” — neither just one dialect, nor a supra-dialectal national variety, nor yet a dictionary devoted to a lexicon comprising a few dialects — encounters challenges of scope, evidence, and representation largely unfamiliar even to the editors of dictionaries like the Dictionary of Newfoundland English, the Dictionary of American English (DAE), or the Dictionary of Jamaican
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".