Learning Through Crisis Epistemologies: Recognising, Managing and Designing New Spaces and Bodies
Bibliographic record
Abstract
The Covid-19 crisis made spaces for people to immerse themselves in moments of reflection. The suspension of time, sites, and body mobility, the collapse of the past principles; as the macro learning environment has undergone unprecedented changes, how could people read and react to those changes? Learning at the university, almost all the students have to adopt an online format as a singular way to access higher education, which calls for more self-management capacities and learning autonomy. Bodily learning is crucial from the pedagogical perspective, drawing insights from The Affective Turn, where Clough (2008) took the human body as biomedia so as to affect learning and transform knowledge. This paper shines a light on the new bodies and spaces with inherent innovation potentiality. Based on the literature review chiefly from Sociology, Anthropology, Philosophy, and Culture Studies, this paper engages with four typologies of learning epistemology, nomad, heterotopia, liminality, and rhythm. Their essential characteristics, principles, and interpretations imply in-between and transformational traits, challenging the existing principles and being open to alternatives. They help evaluate the changes and foster our critical and creative learning in risk and crisis. Simultaneously, they serve as the theoretical foundation for the following innovation fieldwork.
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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.038 |
| Scholarly communication | 0.020 | 0.031 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".