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.
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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.009 | 0.019 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.032 | 0.008 |
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".