Remaining Human in COVID-19: Dialogues on Psychogeography
Bibliographic record
Abstract
Abstract Post-COVID-19 environments have challenged our embodied identities with these challenges coming from a variety of domains, that is, microbiological, semiotic, and digital. We are embedded in a new complex set of relations, with other species, with cultural signs, and with technology and venturing further into an era that pushes back on our anthropocentrism to create a post-human dystopia. This does not imply that we are less human or forfeit ethics in this state of flux, but can lead to considering new ways of being alive and humanists. The aim of this project was to explore walking through our associated psychogeographies as captured in photographs and text from individual walks, as the means by which to characterize responses to the distress of the pandemic and to assess resistance to non-being. The psychogeographies were the starting points for our dialogic enquiry between authors who each represent living theory, representing their own emergent knowledge, inseparable from personal commitments and history. Walking and the associated images and reflections, provided a way to regulate our affect, reconnecting with our bodies, leading to understand and adapt to new meanings of context and ways of coping and healing in this new becoming. The interdisciplinarity of philosophy, social psychology, botany, and clinical psychology is nonetheless rejected in favour of multi-vocality; each author representing their own emergent, living theory, inseparable from personal commitments, and history.
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.021 | 0.072 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.005 | 0.008 |
| 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".