THINK IT THROUGH: FOSTERING AESTHETIC EXPERIENCES TO RAISE INTEREST IN LITERATURE AT THE HIGH SCHOOL LEVEL
Bibliographic record
Abstract
French Quebec literature has always served as a cultural reference for the Quebec curriculum, and is still the means through which French language skills are most often taught in secondary 5 (grade 11) Quebec classrooms. In this paper, I suggest an interactive teaching of the Quebec play Incendies in three secondary 5 classrooms. Students’ meaning making responses, as elicited by qualitative data, provide an understanding of directions a teacher might take to increase interest in reading. These patterns of reactions further equip teachers with concrete tools to guide students in their reading. The purpose of this mixed methods study, in which quantitative data supported qualitative findings, was to see whether an intervention using personal maps (i.e., aesthetigrams) would increase interest in French Quebec literature. To this end, pre-tests, a map making intervention, and post-tests were given to 71 female high school participants in response to scene 37 of Incendies. The researcher pre-identified four main categories that guided students in their response process to literature. This process allowed students to reflect on their personal values and develop their agency in their learning. The following article informs researchers and practitioners alike of the effectiveness of aesthetigrams in literature pedagogy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".