A Corpus Study of the English Suffixes -ness and -acy: Productivity, Genre, and Implications for L2 Learning
Bibliographic record
Abstract
Despite substantial scholarship relating to word structure (Anderson, 2018), for English affixes the relationship between productivity, genre, and second language (L2) learning remains unclear. Analysis of the existing literature reveals that deadjectival noun suffixes (i.e., nouns derived from adjectives such as appropriacy or goodness) have been underexamined. To address this gap, we examine two rival suffixes, -acy and -ness, through the lens of Construction Morphology (Booij, 2010), considering numerous factors which might condition their varying usage. Critically, corpus data in the Corpus of Contemporary American English and the British National Corpus (Davies, 2008-) reveal the importance of considering these affixes’ productivity in relation to genre, since -acy is especially frequent in academic texts, principally within certain social sciences. The implications for learners and teachers of English as a second language are discussed, particularly higher-level learners building communicative competence in academic contexts, along with a preliminary learner corpus comparison of the two variants.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".