Bibliographic record
Abstract
The need for grade scaling typically emerges in the context of an assessment of student work based on relatively objective or fixed subjective criteria that produces a distribution of results that the instructor believes to be problematic in some sense; e.g., a multiple-choice test in which a large enough proportion of the students did so poorly that, left unscaled, it is likely to deter them from putting further effort into the course.Even instructors who believe that grade scaling is pedagogically unsound may, from time to time, be faced with the practical reality that, all things considered, it is a necessary evil.As such, a good understanding of the options that instructors have open to them in this matter would seem to be essential.In this paper, I discuss issues surrounding the scaling of grades as well as the relative merits of different approaches to doing so.The main issue dealt with concerns the justification for grade scaling on pedagogical grounds.This takes us some distance in establishing a set of axioms that inform the choice of a general approach to grade scaling.Next, I show that, among seven different approaches, including five that are fairly well known and one that is entirely new, only the latter satisfies all of the axioms.Finally, I show that the new approach can be used as a "self-scaling" technique for adjusting course grades to reflect class participation in a manner that is non-detrimental to students who reach a minimum standard and differentially beneficial to students who are closer to the pass-fail boundary (relative to those who are further from it, in either direction) on the basis of the other required elements of the course.
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 | 0.104 | 0.268 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.008 | 0.040 |
| Scholarly communication | 0.022 | 0.021 |
| Open science | 0.012 | 0.011 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".