Humanizing with Humility: The Challenge of Creating Caring, Compassionate, and Hopeful Educational Spaces in Higher Education
Bibliographic record
Abstract
Leading with care and compassion, critically reflecting on our teaching practices, and collaboration has always been central to our pedagogical practices. Participating in the #ONHumanLearn project, an initiative designed to humanize learning in higher education, we began to notice a growing divide between our engaged and disengaged students. As we learned/unlearned/relearned to take our professional practice one step further, we started to notice our own sense of powerlessness intensify alongside feelings of fatigue and frustration for our inability to reach the disengaged. We wondered what we could be doing differently to reach them. As we reflect on the process, we humbly accept that leading with care also means caring for ourselves, and that any initiative working to humanize higher learning ought to firmly embed and embody co-learning as a relational and reciprocal approach. In this paper we pay attention to inequities that became more apparent or were created as we sought to humanize education, the opportunities we have found, and our developing awareness of what is needed to sustain change.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".