Bibliographic record
Abstract
Salsa dancing, a partnered dance premised on the felt sense of connection, is well suited to an exploration of Henry’s radical phenomenology of immanence and Heidegger’s facticity of life. Birthed in social celebratory contexts, salsa carries a particular motile freedom. What matters most is not how the dance movements are created from an outer frame of reference, but the experience of interactive responsiveness that emerges from unanticipated acts of giving life to another. Connecting to one’s partner and exuding a presence filled with life is revealed in an indepth interview with two-time world champion salsa dancer, judge, choreographer and coach, Anya Katsevman. This interview attempts to invoke the kinetic, kinesthetic and affective registers of the lividness and livingness of salsa dancing. As a phenomenological inquiry into factical life, the inter-view is presented not so much as a matter of shared perspectives or viewpoints, but more in the way of an inter-feeling, a practice of life engagement. This affectively-oriented approach provides both promise and challenge to the field of phenomenology. It invites us to delve more deeply into feeling acts of seeing. It also helps us understand how, through attending more fully to acts of seeing, we can increase the intensity with which we feel the upsurge of life.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.033 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".