Words We Would Want: Comparison of Three Pre-programmed Vocabulary Sets With Frequently Used Words in English
Bibliographic record
Abstract
Abstract In this descriptive study, three pre-programmed vocabulary sets—Picture WordPower 45 location (Inman Innovations), Unity 45 Full vs. 4.06 (Prentke-Romich Company), and Gateway 60 vs. 1.06.18 (Dynavox Technologies)—were examined for word-based vocabulary content and keystrokes per word. The vocabulary contents of the each set were then compared to the thousand most common words as identified by two different listings apiece, that published in Word Frequencies in Written and Spoken English based on the British National Corpus (BNC), and Wiktionary TV/Movie Frequency Lists (2006). The pre-programmed vocabulary set best representing these frequency lists was Unity 45 Full, followed by Gateway 60 and Picture WordPower. The vocabulary sets using the fewest average keystrokes per word, based on frequency lists, were Picture WordPower and Gateway 60 followed by Unity 45 Full. Results provide an aid for evaluating the comparative merits of pre-programmed vocabulary sets, such as inclusion of frequently used English words and relative keystroke savings.
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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 | 0.002 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".