Computing the vocabulary demands of L2 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
Linguistic computing can make two important contributions to second language (L2) reading instruction.One is to resolve longstanding research issues that are based on an insufficiency of data for the researcher, and the other is to resolve related pedagogical problems based on insufficiency of input for the learner.The research section of the paper addresses the question of whether reading alone can give learners enough vocabulary to read.When the computer's ability to process large amounts of both learner and linguistic data is applied to this question, it becomes clear that, for the vast majority of L2 learners, free or wide reading alone is not a sufficient source of vocabulary knowledge for reading.But computer processing also points to solutions to this problem.Through its ability to reorganize and link documents, the networked computer can increase the supply of vocabulary input that is available to the learner.The development section of the paper elaborates a principled role for computing in L2 reading pedagogy, with examples, in two broad areas, computer-based text design and computational enrichment of undesigned texts.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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