The hyphen as a segmentation cue in triconstituent compound processing: It’s getting better all the time
Bibliographic record
Abstract
Inserting a hyphen in Dutch and Finnish compounds is most often illegal given spelling conventions. However, the current two eye movement experiments on triconstituent Dutch compounds like voetbalbond"footballassociation" (Experiment 1) and triconstituent Finnish compounds like lentokenttätaksi"airporttaxi" (Experiment 2) show that inserting a hyphen at constituent boundaries does not have to be detrimental to compound processing. In fact, when hyphens were inserted at the major constituent boundary (voetbal-bond"football-association"; lentokenttä-taksi"airport-taxi"), processing of the first part (voetbal"football"; lentokenttä"airport") turns out to be faster when it is followed by a hyphen than when it is legally concatenated. Inserting a hyphen caused a delay in later eye movement measures, which is probably due to the illegality of inserting hyphens in normally concatenated compounds. However, in both Dutch and Finnish we found a learning effect in the course of the experiment, such that by the end of the experiments hyphenated compounds are read faster than in the beginning of the experiment. By the end of the experiment, compounds with a hyphen at the major constituent boundary were actually processed equally fast as (Dutch) or even faster than (Finnish) their concatenated counterparts. In contrast, hyphenation at the minor constituent boundary (voet-balbond"foot-ballassociation"; lento-kenttätaksi"air-porttaxi") was detrimental to compound processing speed throughout the experiment. The results imply that the hyphen may be an efficient segmentation cue and that spelling illegalities can be overcome easily, as long as they make sense.
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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 | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".