Beyond Broudy’s Triad—Infusing University Students with the Love of Poetry
Bibliographic record
Abstract
In spite of the diverse schools of thought providing guidance for poetry teachers—such as the didactic, heuristic, or phyletic approaches—this myriad of teaching modes has failed to generate adequate student appreciation for poetry courses. The reason for this is teachers’ tendency to cling to the idea that one must choose a particular approach and find out the correct or fixed meaning. This study includes a recommendation for a major shift in teaching poetry that transforms each class session into a new learning rather than a teaching experience—one in which the instructor’s role is to inspire a passion and love for poetry in ESL learners. This teaching-learning style requires that teachers change from being omniscient sages to participants, co-explorers, and learners—a move from teaching methods to learning styles and a shift from encouraging the love of teachers to inspiring the love of poetry in university students.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.027 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".