Tumbling from Embodiment to Enfleshment: Art as Intervention in Collective Autoethnography
Bibliographic record
Abstract
We, the four authors, found ourselves swept into the tenure process, tumbling as we inquired into what this transition meant to each of us and to all of us. Through a methodological grounding in collective autoethnography – and expanded by art intervention, we came together in our inquiry to explore key experiences as new professors, asking how we individually, collectively, and aesthetically move(d) through our transitions into tenure track assistant professorship. We found it was through the embodied acts of listening, attuning, and responding with/in our flesh as women and as researchers that we felt the friction of Tenure as another body in our collective. Tenure provoked our poems, tears, arguments, victories, aches, paintings, tenderness, stitches through fabric, movements, and identities. This article serves as a methodological unpacking of our arts-based research process that used Tumblr, individual and collective artmaking, and visits to each other’s homes. While our collective work seeks new potentialities of understanding our tumbling selves as women, artists, and researchers new to the academy, we also see this work as opening our stories to the world in order to create new possibilities beyond our project.
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 | 0.020 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.037 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".