Bibliographic record
Abstract
This article sets forth the first comprehensive defense of mandatory curves. It begins with a case study of one law school. That institution lacked formal grade normalization policies during the period of the case study. As a result, the school suffered from dramatic grade disparities. This article contains a list and statistical analysis of the most significant disparities. The statistical analysis supports the conclusion that the grade disparities were caused by differences in teacher grading philosophy, and not by student merit or any other factor.\nNext, this article presents several arguments in favor of mandatory curves. The most crucial is that grade variances that flow from differences in professor grading philosophy are grossly unfair to students. A second important defense of forced curves is that grade disparities distort the process of course selection, inducing students to register for classes based on the grading practices of the professor rather than on substantive concerns, such as topical importance, career relevancy, and skill development.\nThe article then responds to the eight most significant and common objections to mandatory curves. Several of these objections are deeply problematic, such as the contention that curves prevent professors from awarding students the grades they deserve. Others have some merit, such as the argument that mandatory curves encourage excessive competition among students. But the latter set of criticisms ultimately does not undercut the case for curves, principally because mandatory curves are the lesser evil. For example, the competition objection fails because the significant grade disparities that frequently result in the absence of a curve probably causes more competition than mandatory curves do. And even if forced curves do increase competition, the unfairness of grade disparities flowing from differences in professor grading philosophy is the more pressing concern. Finally, the article ends with a discussion of some issues regarding the structure and scope of mandatory curves, including the applicability of curves to smaller classes, seminars, and clinics.
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 | 0.025 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.026 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.012 | 0.025 |
| Insufficient payload (model declined to judge) | 0.011 | 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".