Contesting Our Taken-for-Granted Understanding of Student Evaluation: Insights from a Team of Institutional Ethnographers
Bibliographic record
Abstract
Educating for nursing excellence can be demanding and challenging work. One of the troubling centers of attention for nurse educators is their evaluation of nursing students in practice. This article outlines some of the problems nurse educators encounter in evaluation work and uses the theoretical framework of institutional ethnography to disrupt some of the conventional explanations that mediate what happens in teaching and evaluation work when students fail to meet the required standards. In developing our analysis, we describe our research process and provide details about how we used some of the methodological conventions of institutional ethnography. The early findings from this project provide an alternate knowledge of our evaluation practices, which demands that we question our taken-for-granted understanding of due process and what it is accomplishing. Our findings raise compelling ethical questions that guide nurse educators to rethink our evaluation work.
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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 | 0.087 | 0.137 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.025 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".