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 Recently there has been some debate about the appropriacy of different lexical units in pedagogy and research (e.g., Brown et al., 2020; Dang & Webb, 2016a; Kremmel, 2016; Laufer & Cobb, 2020; McLean, 2018; Nation, 2016; Nation & Webb, 2011; Vilkaitė-Lozdienė & Schmitt, 2020). The lexical unit (word types, lemmas, flemmas, word families) needs to be considered when developing wordlists, vocabulary tests, and vocabulary learning programs. It is also central to the lexical profiles of text and corpora, which indicate the vocabulary learning targets associated with understanding different types of discourse. Perhaps most importantly, the lexical unit of words found in vocabulary learning resources such as word lists and tests may affect their pedagogical value. The aim of this article is to highlight aspects of research and pedagogy that are affected by lexical units and describe issues that should be considered when operationalizing words in studies of vocabulary and learning resources.
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 | 0.001 | 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) | 0.079 | 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 it