The Nuclear Word Family List: A List of the Most Frequent Family Members, Including Base and Affixed Words
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 This article introduces the NFL7 (Nuclear Family List 7), a list of the 2,887 most frequent “nuclear” word families, that is, families that include just the most frequent family members and exclude those that constitute less than 7% of family occurrences. The NFL7 was developed by using a dedicated computer program, the Nuclear List Builder (freely available to users). To construct the list, we used that tool to reduce the complete BNC/COCA lists of the 3,000 most frequent word families from 19,062 to 7,293 word types and from 9,132 to 5,610 lemmas. Despite this reduction, the NFL7 compares favorably with other lists in terms of text coverage, and it includes a small number of the most frequent derivational affixes. We argue that the nuclearization of the list makes it suitable for nonadvanced learners, for teaching and testing both receptive and productive knowledge, and for instruction in basic morphology.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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