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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".