Bibliographic record
Abstract
PROCESSING INSTRUCTION: THEORY, RESEARCH, AND COMMENTARY. Bill VanPatten (Ed.) . Mahwah, NJ: Erlbaum, 2004. Pp. 360. $79.95 cloth. I have often been struck by how highly fluent second language (L2) speakers of English can make errors in, say, possessive determiner gender agreement (e.g., Chinese, French, or Russian speakers saying “his” instead of “her”) without being disturbed at all by what they have said. To me, as a first language speaker of English, the error is extremely jarring and can disrupt understanding. For the L2 speaker, the error has much less impact. By contrast, an error in lexical reference (e.g., saying “boy's” instead of “girl's”) is generally experienced as jarring and potentially disruptive, even by L2 speakers. Why, then, do L2 speakers perceive errors in linking grammatical form to meaning so differently than errors in linking lexical units to meaning? Does this difference pose a challenge for L2 instruction and, if so, how should the challenge be met?
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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.023 | 0.023 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".