English Zero Derivation Revisited: Nouning and Verbing in Online Business Articles
Bibliographic record
Abstract
Zero derivation is a word-formation process when from a word in a certain lexical (sub)category by adding a zero derivational affix, but with allowed inflectional interventions, another, new lexeme is created, with absolutely same form (from a derivational point of view); similar, expanded meaning; and, most importantly, belonging to a different lexical (sub)category. The analytical structure of English makes this a very frequent, productive, and economic process, across almost all categories, with the noun to verb and the verb to noun directions marking the most common process formations. Yet, regardless of the direction, the newly formed, zero derived lexeme belongs to a different lexical (sub)category not only based on the same form but on the similar semantics that the old and the new lexemes share, due to the meaning transfer through cognition. Having seen that the process of zero derivation is present and widespread in everyday life, this paper aims at researching its presence and productivity in online business articles, that is, in online texts which discuss business topics. Online media have been chosen since its quest for timely information requires fast expression and, in such a need, quick word-formation processes, like zero derivation, are in place, making the expression formally short but semantically expanded. Therefore, it is the cognitive transfer of meaning that drives the process. Similar to the reason for selecting online media, business articles have been used as a corpus, to show what language is used when discussing non-language-centred topics, that is, business.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".