Bibliographic record
Abstract
Imagine spending six or more years diligently training in a particular subject to only apply for a job in an unrelated field. Most everything you know will never be used; your education remains for your own edification, locked in a dusty wardrobe of the mind. Add to this a lack of awareness of how to do your new job. This is the picture of the modern-day college composition teacher. Newly printed Ph.D.s (and sometimes Masters) apply for positions for freshmen composition with very little pedagogical training, background, or awareness of the task. For them, composition is a backup plan in the event that their preferred occupation (usually as professor in the humanities) does not pan out. Students in freshmen composition series across the United States end up paying the price for the limited pedagogical preparation that many teachers have had. These students should not have to wait five or ten years before experience teaches these instructors how to be excellent in their craft. This is a silent institutional problem of massive proportions that can—and should—be fixed. This article offers tangible solutions to the issues involved in the lack of pedagogical training of our newly minted Ph.D. students.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".