Is there a hip or a pie in hippie?
Bibliographic record
Abstract
Abstract Pseudo-compound words (e.g., hippie ) are words that look like compound words (e.g., snowman ) but, in fact, do not have the morphemic structure of a compound word. For instance, the pseudo-compound word hippie has hip and pie embedded in it, but they do not function as morphemes. Pseudo-compound words vary in terms of phonological transparency. Some, such as pumpkin , are phonologically transparent because the pronunciations of pump and kin are maintained when these pseudo-constituents become part of pumpkin . In contrast, the pseudo-compound word carrot is phonologically opaque because the pronunciations of car and rot change when they are embedded in carrot . Previous studies have demonstrated that compound words go through morphological decomposition and attempts at meaning construction during written production tasks. For instance, compound words are not output as single units during typing tasks, but rather are typed in chunks based on their morphology (e.g., snowball is typed in two parts: first as snow and then as ball ), which results in an increase in typing latencies at the morpheme boundary (i.e., between the last letter of the first constituent and first letter of the second constituent). The same is true for pseudo-compound words, even though these words do not have the morphemic structure of a compound word. Given that previous research has shown that the morphological decomposition of compound words during typing tasks looks different depending on the semantic transparency of the compound word’s constituents (i.e., the degree to which the meaning of each constituent of a compound word contributes to the word’s overall meaning), we wanted to examine whether the level of phonological transparency of the pseudo-constituents of a pseudo-compound word influences typing latencies at the pseudo-morpheme boundary.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".