The synchronic status of historical bound roots in the mental lexicon
Bibliographic record
Abstract
Abstract Many English words contain historical roots that do not occur as free morphemes (e.g., nov in innovate, dict in verdict ). These words often retain an appearance of compositionality and are associated with effects on lexical processing ( Pastizzo & Feldman, 2004 ; Taft & Forster, 1975 ), but frequently their roots are difficult to identify without recourse to historical etymologies, and they are semantically opaque and unproductive. More practically, although such words are prominent in academic vocabulary, they are often difficult to learn, and instruction inspired by their apparent morphological structure has yielded mixed results ( McKeown et al., 2018 ). We explore these psycholinguistic and educational challenges through a dynamic view of the mental lexicon ( Libben, 2022 ), understanding morphological resources as gradient, emergent, and contextually adaptable for meaning making. We quantified bound roots’ morphological families by training an unsupervised parser on a lexicon approximating that of an educated English user, and then assessing polysemy and coherence of roots’ meanings, using vector semantic representations. Testing against behavioral data supported the validity of these measures, suggesting new ways of measuring the properties of bound roots independent from etymological data and demonstrating sensitivity even to unproductive morphological structure, that can support academic vocabulary development and meaning-making.
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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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".