Everything is Psycholinguistics: Material and Methodological Considerations in the Study of Compound Processing
Bibliographic record
Abstract
The specific domain that I discuss in this paper is the processing of compound words. This research area is no more than 30 years old and my own involvement in it has been for about half that time. The study of how compound words are processed is a very specific sub-field, considerably narrower than the domains of which we would normally consider. Yet, for a variety of reasons which I discuss below, compound processing research finds itself associated with issues that are central to our views of the fundamental nature of lexical processing, and the enterprise of psycholinguistic research itself. These are certainly the domains of which we do want to take stock. My goal in this article is to address issues that are of general interest through the medium of research on the representation and processing of compound words. In considering the development, current state, and likely future trajectory of a specific research domain, this provides us with an opportunity to gain a broader perspective on the field as a whole and to step outside (and hopefully above) the normal course of research activity.
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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 | 0.098 | 0.200 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.008 | 0.027 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| 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".