Bibliographic record
Abstract
This paper investigates the role of new combining forms in the formation of neologisms which are currently expanding the lexicon of English for Special Purposes (ESP). In the past, only neoclassical combining forms, such as initial bio- or final -logy (in biology), were productively used in ESP. Nowadays, specialized combining forms also include abbreviated forms of existing words (e.g., cyber- from cybernetic in cyber-attack), as well as secreted (i.e. reinterpreted) forms (e.g., -bot from robot denoting ‘a type of automated program or software’ in knowbot). The paper explores a set of combining forms attested since the second half of last century in the online version of the Oxford English Dictionary (OED) with the aim to demonstrate how specialized sectors, such as science or information technology, are being enriched by series of combining-form combinations. The paper conducts quantitative analyses in the Corpus of Contemporary American English (COCA) and the News on the Web Corpus (NOW) to substantiate the frequency and stability of specialized combining forms and their profitability in the formation of both novel and nonce words.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.017 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".