Bibliographic record
Abstract
What does realist theory have to say about the evaluation of students’ performance in the humanities classroom? My question is motivated by the concerns students have expressed to me with the grading systems in their courses, and my own sense that realist theory might provide guidance in addressing these concerns. Particularly in qualitative fields such as English literature, the field in which I work, there is a fairly common perception among students that grade determinations are subjective, owing more to how well their ideas correspond with their instructor’s than to their ability to produce work that meets a coherent set of course objectives. And little wonder. In many courses, little or no written documentation is ever provided describing the course objectives, the criteria by which individual assignments are assessed, or the relative weight of assignments in the determination of final grade. Factors such as “class participation” may constitute up to a quarter of a student’s final grade without ever being explicitly defined, much less presented in a manner that explains why such factors should be relevant in the determination of grades. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.174 | 0.225 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.003 | 0.034 |
| Scholarly communication | 0.022 | 0.023 |
| Open science | 0.009 | 0.009 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".