“Can I have a grade bump?” The Contextual Variables and Ethical Ideologies that Inform Everyday Dilemmas in Teaching
Bibliographic record
Abstract
Educators are regularly confronted with moral dilemmas for which there are no easy solutions. Increasing course sizes and program enrolments, coupled with a new consumerist attitude towards education, have only further exacerbated the quantity and quality of students’ requests for special academic consideration (Macfarlane, 2004). Extensions, late submissions, and grade bumps—once rare—are now commonplace. However, there is very little in the pedagogical literature that addresses these everyday dilemmas. In a culture of transparency, unspoken policies that address these requests are the form of learner consideration that is the least transparent to students and educators alike. Here we explore some of the variables that contribute to the complexity of these dilemmas, and the ethical ideologies that can inform their resolution. Our goal is not to provide best practices, but rather to facilitate reflection about how individuals make these decisions. The idiosyncratic nature of these decisions can be framed as a reflection of different ethical ideologies, and we describe one approach to framing individual ethical ideologies from the business literature. Finally, we consider whether faculty should be making these decisions at all, using the centralization of academic integrity (cf. Neufeld & Dianda, 2007) as a model, and explore its parallels with issues around ethical dilemmas in teaching.
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.024 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".