Meaning‐Inferencing Versus Meaning‐Given Procedures: The Case of Idioms
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 Inferring the meaning of words and then verifying one's interpretations is widely believed to create relatively strong memories of the items. According to the available research, it is when the inferences are accurate that the learning outcomes are the most promising. The present study extends this inquiry to idioms. Fifty‐six ESL learners were presented with 21 English idioms (e.g., toe the line ) in brief contexts and they were either prompted to infer the meaning of each idiom or they were given the meaning directly. After each inferencing attempt, the correct meaning was given as feedback. This initial learning stage was followed in the same session by a meaning‐recall task where the learners were again given the correct meanings as feedback. The results of a posttest administered one week later indicate that prompting learners to make inferences is beneficial compared to directly giving the meanings on condition that the inferencing was successful.
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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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