Bibliographic record
Abstract
1. Foreword 2. Contributors 3. Articles 4. Pauses as indicators in story structure (Benin) (by Grool, Marjolijn) 5. Predicting intelligibility and perceived linguistic distance by means of the Levenshtein algorithm (by Beijering, Karin) 6. The Early Surinamese Creoles in the Suriname Creole Archive (SUCA) (by Berg, Margot van den) 7. VP-Internal DPs and Right-Dislocation in Zulu (by Buell, Leston) 8. How universal is the Universal Grinder? (by Cheng, Lisa Lai-Shen) 9. Proper names used as Common Nouns in Belgian Dutch and German (by De Clercq, Karen) 10. Preposition stranding in development (by Coopmans, Peter) 11. Preliminary remarks on object-marking in Makalero (by Huber, Juliette) 12. Language Attrition in Dutch Emigrants in Anglophone Canada: Internally or externally-induced change? (by Keijzer, Merel C.J.) 13. NPI-licensing and dependent tense in Serbian (by Milicevic, Natasa) 14. Czech modal existential wh-constructions as vP-level free relatives (by Simik, Radek) 15. Intensive plurality: Hausa pluractional verbs and degree semantics (by Souckova, Katerina) 16. Mutual intelligibility of Chinese dialects tested functionally (by Tang, Chaoju) 17. The encoding of adjectives (by Verkerk, Annemarie) 18. The placement of bare plural subjects in Dutch (by Vogels, Jorrig)
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.352 | 0.288 |
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".