Bibliographic record
Abstract
This project aims to assist the development of a grading rubric for marking essays in a biomedical ethics course although the process outlined can be applied to any multi-TA course. Guaranteeing grading consistency across teaching assistants (TAs) in courses with multiples TAs is challenging. Multi-TA courses are particularly challenging to maintain the consistency among TAs in respect to, for instance, teaching strategies and grading essays. Past teaching experiences, educational background, teaching perspectives, gender, and cultural background are some reasons why inconsistencies happen.\nIn an attempt to overcome this challenge, this project proposes a session that brings together TAs and the course instructor to develop a grading rubric that both makes sense for all TAs and meets the instructor’s expectations of the content and format of the essay. It is true that TAs must have autonomy in terms of teaching strategies; however, some consistency in the grading process is necessary to guarantee fairness to all students. Research suggests that grading rubrics help TA to consistently mark students’ written assignments. Furthermore, rubrics help TAs to provide constructive feedback to students and guarantee that students know the how their assignments will be marked.
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 | 0.029 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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".