Bibliographic record
Abstract
Valuing care and self-care in higher education requires a conscious pause and rethinking of how we are together as educators and students. The pandemic caused various complexities, including changes in curriculum delivery, deadlines, and assessment modes, leading to feelings of overwhelm, anxiety, and change fatigue, which contributed to the emergence of panicgogy. This paper argues for the need to disrupt this way of being and experiencing the pandemic through valuing humanity and repositioning self-care and care by and for academics to inform their pedagogy. Presented is the narrative and the design story behind Pedagogy of Belonging (PoB), a systems informed framework that prioritizes relationships and humanizes teaching by placing students and educators at the center of curriculum and pedagogical decisions. PoB provides a pedagogical framework for progression throughout the teaching cycle. The paper suggests that PoB is a conscious pause, a contemplative practice that interrupts panicgogy, collective exhaustion, and disconnect in higher education. PoB repositions holistic education at the heart of what educators do, facilitating interconnectedness and encouraging a rethinking of care and self-care in higher education through the domain of belonging underpinned by relationships, engagement, and communication.
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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.058 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.001 | 0.020 |
| Research integrity | 0.003 | 0.010 |
| 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".