On Derivational Productivity in Slovene with Notes on Lexical Frequency and Awareness of the Norm
Bibliographic record
Abstract
The major aim of the study reported here was to discover a way of measuring the productivity of derivative patterns or processes, a task which appears never to have been accomplished for any language before. As specific material for the study, two contrasting deadjectival verbal derivative patterns were chosen: the 'inchoative' -eti and the 'factitive' -iti, as occurring in, e.g., rjav 'brown', rjaveti 'to become brown', rjaviti 'to make (someone or something) brown.' The method involved psycholinguistic tests, administered in Ljubljana in 1993—94. The subjects for the tests in 1993 were 186 secondary and 180 post-secondary students. For the 1994 follow-up study the tests were limited to three groups of university respondents, totalling 116 in all. Cues comprised four questions: (1) Ali se po vašem beseda nahaja v knjižni slovenščini? (2) Ali vi to besedo kdaj uporabljate? (3) Ali je ta beseda po vašem možna in razumljiva? (4) Kako pogosto sami uporabljate to besedo? Subjects selected responses on five-point Likert scales; the data were analyzed statistically. As far as productivity is concerned, there are three conclusions. First, it is clear that, at least for these derivative patterns, productivity is a "cline,'' but in two quite distinct meanings of the term. First and most obviously, one process may be synchronically more productive than another. Second, what may be called the productive strength of any one process also varies. To take just two examples from the mean responses to question (3), we see that the cue godneti was assessed, on average, as extremely "possible and understandable," while at the other end of the scale the cue plašeti was assessed, on average, as well nigh impossible and incomprehensible; the remaining cues are strung out along the cline in between the two.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".