How Much Knowledge of Derived Words Is Needed for Reading?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The study explores the usefulness of the word family as the unit of counting in studies of lexical coverage and comprehension. It determines the proportion of texts covered by the various members of a word family, that is, basewords, inflected words, and derived words, and analyzes the contribution of the affixed words to lexical thresholds. This exploration was performed by a text analysis computer program called Morpholex that analyzes the entire lexis of an entered text, pulling out all words bearing prefixes and suffixes and counting the unaffixed words as basewords. We analyzed a variety of texts, academic and narrative, authentic and simplified, and calculated the number and percentage of basewords and affixes in each text. We also located the most frequent affixes in our text corpus and demonstrated which affixes and how many contributed to 95 per cent and 98 per cent text coverages. Our results show that reaching the lexical thresholds for reading does not require the knowledge of most of the derived words in a word family since a small number of frequent affixes will provide the necessary coverage together with the basewords and inflections.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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 it