Analysis of stance and understanding in sixth graders' written responses to literature in different instructional settings.
Bibliographic record
Abstract
This study examined the relationship between literature instruction and students' written responses to literature. One hundred and ninety-nine students from ten grade six classrooms were asked to write fire written responses to a short piece of realistic fiction. Their teachers completed the Literature instruction Profile and were assigned a score placing them In either a "high score" or "low score" category. Student responses were then analysed for stance and level of understanding. Crosstabs testing indicated that significant relationships existed between student stance, level of understanding and teachers' instructional styles. The aesthetic stance focusing on the reader's personal experience with the text was associated with higher levels of understanding. Moreover, a more aesthetic instructional style was linked with aesthetic response stance and higher levels of understanding for students. While student response stance and understanding were not found to be associated with gender or the use of artistic expression, longer student responses were shown to be related to aesthetic response stance and higher levels of understanding. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2000 .S83. Source: Masters Abstracts International, Volume: 39-02, page: 0334. Adviser: Larry Morton. Thesis (M.Ed.)--University of Windsor (Canada), 2000.
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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 | 0.002 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".