Filler syllables as precursors of referring expressions
Bibliographic record
Abstract
Abstract In this chapter, we examine the properties of filler syllables as transition forms in the development of referring expressions. In particular, we hypothesize that fillers are precursors of referring expressions. We focus on the distribution, the phonological form and the referential function of fillers in prenominal and/or preverbal positions, in comparison to others forms in these positions. Results show that first, the substantial presence of fillers does not lie in lexical factors, and that they are used in combination with other prelexical forms. Second, their variable realizations are not due to a phonological deficit, and they also exhibit paradigmatic patterning with the use of specific consonants. Fillers also share some of the functional characteristics of grammatical units, since their distribution and presence suggest that they play a role in the construction of the verbal and nominal categories. Moreover, in the preverbal position, children’s use of fillers varies according to the topic of the utterance. In conclusion, filler syllables exhibit the formal and functional characteristics of a transitional category and an adult-like paradigm of referring expressions at the same time, and should be studied as such.
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 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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".