The Power of Words: Essays in Lexicography, Lexicology and Semantics. In Honour of Christian J. Kay
Bibliographic record
Abstract
Introduction C. P. BIGGAM: Old English colour lexemes used of textiles in Anglo-Saxon England Julie COLEMAN: Slang terms for money: a historical thesaurus Fiona DOUGLAS and John CORBETT: 'Huv a wee seat, hen': evaluative terms in Scots Philip DURKIN: Lexical splits and mergers: some difficult cases for the OED Andreas FISCHER: Of faederan and eamas: avuncularity in Old English Roger LASS and Margaret LAING: $ho:fian{*}/vK2: a LAEME-based lexical study Caroline MACAFEE: The rhyme potential of Scots Terttu NEVALAINEN and Heli TISSARI: Of politeness and people Michiko OGURA: ME douten and dreden Jane ROBERTS: What did Anglo-Saxon seals seal when? Jeremy J. SMITH: Notes on the medical vocabulary of John Keats Jane STUART-SMITH and Claire TIMMINS: 'Tell her to shut her moof': the role of the lexicon in TH-fronting in Glaswegian Louise SYLVESTER: Forces of change: are social and moral attitudes legible in this Historical Thesaurus classification? Irma TAAVITSAINEN: Key word in context: semantic and pragmatic meaning of humour James MCGONIGAL: Lexicographical Lyrics Notes on the Contributors Tabula Gratulatoria
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.006 | 0.014 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".