Recognizing the Significant Role of Literature in Teaching College English
Bibliographic record
Abstract
Purely based on my experiential knowledge, this article does not engage with the current so-called “academic” scholarship on the topic. It does not present, according to a critic, “empirically-focused and data-driven research” as the majority of traditional writing studies normally do nor does it “approach and theorize writing as a multidimensional practice and object of study” following what is known as a so-called “methodical analysis.” Free from and unpopulated by unnecessarily top-heavy “academic” and “educational” jargons, this new and original experience-based article, that boasts in not being academically derivative and adulterated, argues that College English (or freshman composition) should be as much literature-based as it is currently based on other writing mechanics related to technology and social media, and practiced through what sometimes seems to be only elaborately and long drawn out steps in the writing process with the assessment criteria impractically divided into minor as well as minute differences. The course should be more open and flexible in its syllabus and be taught with a reading of suitable literary materials as a major component and literature-based writing exercises, among, of course, the other interesting topics of contemporary culture.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.024 | 0.010 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".