Redefining Academic Safe Space for Responsible Management Education
Bibliographic record
Abstract
Abstract In a time of increasing polarization, how can we address sensitive topics and ensure that university classrooms remain places of healthy discussions and ethical deliberations? This paper addresses this important question by drawing on unique qualitative data from our students’ accounts of their experience in an organizational ethics course. We developed the course using a novel pedagogical strategy centered around the creation of an artistic portfolio. We find that student engagement in an alternative individual space, such as the artistic portfolio, supports them in developing (inter)personal skills in preparation for constructive participation in sensitive discussions and ethical deliberation in the classroom. Additionally, engagement with the artistic portfolio provides them with an alternative means for alleviating tension that arises from these discussions and a space for expressing their opinions. Our findings highlight the role of the portfolio as an individual safe haven that supports teachers in facilitating a positive classroom atmosphere and guides students through challenging discussions and deliberations intrinsic to responsible management education. Considering these new insights, we advocate for a shift from a collective to an individual perspective on safety in academia. This transition liberates the classroom from the constraints and limitations often associated with the establishment of collective safe spaces.
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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 | 0.027 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.024 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.003 | 0.027 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".