What Is Wrong With Grade Inflation (if Anything)?
Bibliographic record
Abstract
Grade inflation is a global phenomenon that has garnered widespread condemnation among educators, researchers, and the public. Yet, few have deliberated over the ethics of grading, let alone the ethics of grade inflation. The purpose of this paper is to map out and examine the ethics of grade inflation. By way of beginning, we clarify why grade inflation is a problem of practical ethics embedded in contemporary social practice. Then, we illuminate three different aspects of grade inflation—longitudinal, compressed, and comparative—and explore the ethical dilemmas that each one raises. We demonstrate how these three aspects may be seen as corresponding to three different victims of grade inflation—individuals, institutions, and society—and hence also to three potential agents of harm—teachers, schools, and educational systems. Next, we reflect upon various compelling reasons that these agents inflate grades, whether from an ethic of care, fiduciary responsibility, or simple self-preservation. Subsequently, we consider a variety of means of combatting grade inflation, and invite more educators and philosophers to delve into the complex practical ethics of grade inflation.
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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 | 0.013 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.026 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".