Bibliographic record
Abstract
I follow posthuman pedagogy which recognizes human non-exceptionality, respects history, attunes deeply to the present, and orients to the future. I offer a brief chronology of experiences and ideas that have stuck with me emotionally and impacted my praxis as an artist, scholar, and teacher. My first worlding-storying features Ted Aoki and his gift of care through pedagogical phenomenology, allowing for authentic respectful synergies between the particular and the universal. My second worlding-storying features change, serendipity and community work as a scholar, artist, and teacher in Thunder Bay, Ontario. My third worlding-storying features porous entanglements and connections through teaching thematically across fields of study. My fourth worlding-storying features the synergistic gifts of posthumanism: We exist in collective states of immanence, drawing on natural instincts, where the sensing intuitive embodied self knows how to live, how to feel, how to imagine, and how to die. My fifth worlding-storying features attunement to new materialism, spaces and places as agentic and pedagogical. My sixth worlding-storying features respect for ancient and ancestral ways of knowing. My final worlding-storying features overarching respect for wholism, cross-disciplinarity, and connectedness.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".