Bibliographic record
Abstract
In Western societies, adjudication has long been said to involve the crucial task of putting one’s emotions aside. This so-called dispassionate approach is assumed to result in impartial decision-making vis-à-vis the provision of natural justice and procedural fairness. However, recent research findings on emotions and decision-making do not accord with this assumption. Overall, this body of research, which has grown exponentially over the past 30 years, suggests that emotions cannot be sifted out from decision-making processes. Thankfully, this research also supports the argument that human emotion is a precondition for enacting justice. There are multiple ways that specific emotions can ultimately influence judgment and cognitive strategies can be used to ensure that emotions are leveraged for good (i.e., outcomes in the students’ best interest). Post-secondary institutions have an ethical obligation to support adjudicators in carrying out this emotional work, and the first step is ensuring that adjudicators have comprehensive training on the findings of decision-making research. This professional development session will foster a discussion around this research and, more specifically, focus on strategies for guiding the effects of one’s emotions within the adjudication context.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| 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".