Graves, R. & Hyland, T. (Eds.). (2017). Writing assignments across university disciplines. Bloomington, IN: Trafford.
Bibliographic record
Abstract
For the last three years, I have been part of a team of multi-disciplinary faculty that holds a weeklong workshop each semester for approximately twenty teachers. These teachers, migrating to our cozy space in the library from all corners of campus, have applied—they get paid a modest sum, which is not nothing—to attend our workshop in the hopes of improving their ability to integrate writing assignments into their courses. The workshops are part of a larger initiative, Improving Disciplinary Writing, which was borne out of a needs assessment from our regional assessment body. It is designed to bring together faculty, through workshops and grants, to think collectively about how writing gets taught and ought to be taught differently across and within disciplines. And what we see time and time again is that although each group of twenty teachers is new each semester, and although the ranks consistently vary from adjunct (sessional) to full professor, and although some work in musty chemistry buildings and some in obscure art buildings and some in sleek see-through engineering buildings, the disembodied echoes of frustrations and complaints and discovery and hope and solace from groups past get re-vocalized by groups present. As facilitators, we are not flustered by this fact; rather, we find our own solace in the connection and camaraderie through shared experience happening across disciplines and spaces on campus.
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 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.032 |
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".