Bibliographic record
Abstract
Metaphor research has become widespread. However, students’ understanding of metaphor in informational text has received little study. With increased use of informational trade books in the classroom, research in this area is needed. Fifty-five grade six students with Canadian English as their first language participated in the study. Their understanding of metaphors in excerpts from three recently published informational trade books was examined by the use of the reading think aloud technique and multiple choice activities. One think aloud was completed by each student in both individual and dyadic conditions. Multiple choice activities were completed individually after reading, but with the text available. The think-aloud protocols were examined using specific-trait analysis, holistic scoring, and miscue analysis. The multiple choice activities were scored against anticipated adult-like understanding and the results were subjected to standard statistical tests. Level of understanding of metaphors varied widely among students, with the overall average being about 65%. Contrary to prediction, understanding was significantly higher in the individual condition compared to the dyadic condition. Although part of this difference could be attributed to differences in passage difficulty, the anticipated scaffolding effect of reading with a partner was not found. The reading think aloud was a rich source of information about both the meaning students constructed and the meaning-construction process. The study suggested that the think aloud could be used in the classroom as an effective learning device, particularly in that it allowed less-capable readers to participate as equal partners in what might otherwise have been a frustrating reading task. Overall, there emerged a picture of students at various points along the path to full adult mastery of metaphor, with some students already demonstrating an adult level of understanding. Level of text understanding was consistent with level of metaphor understanding. The only metaphor-type effect identified was for metaphors with copula-verb syntactic-frame structure. Abstractness of the words in the metaphors did not affect meaning construction; however, conventionality of the metaphorical expressions did influence understanding.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".