The position of negative adjectives in Aelfric’s Catholic Homilies I .
Bibliographic record
Abstract
In Old English, negative adjectives, i.e. incorporating the negative prefix -un, are said to generally come in postposition to nouns (e.g. Fischer, 2001; Sampson, 2010). This paper investigates to what extent this general rule is followed in Aelfric’s Catholic Homilies, the texts of this author being a typical choice for the study of Old English syntax (cf. Davis 2006; Reszkiewcz, 1966; Kohonen, 1978). The data have been obtained from the York-Toronto-Helsinki Parsed Corpus of Old English Prose (YCOE). The following research questions have been formulated: Do strong negative adjectives outnumber nonnegated adjectives in postposition? Do strong negative adjectives have a tendency to appear in postposition? Do strong negated adjectives occur in preposition? The results indicated that for the sample analyzed, strong adjectives in postposition are not predominantly negated. Additionally, the postposition of most of those which are may potentially be explained by other factors, such as modification by a prepositional phrase, co-occurrence with a weak preposed adjective (both mentioned by Fischer), or indirect Latin influence in a formulaic phrase. Also, the data does not appear to support the observation that negated adjectives tend to appear in post- rather than preposition.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".