Editorial: Life Phenomenology--Movement, Affect and Language
Bibliographic record
Abstract
The “life phenomenology” theme of the 35th International Human Science Research Conference challenged participants to consider pressing questions of life and of living with others of our own and other-than-human kinds. The theme was addressed by keynote speakers Maxine Sheets-Johnstone, Ralph Acampora and David Abram who invoked a motile, affective and linguistic awareness of how we might dwell actively and ethically amongst human communities and with the many life forms we encounter in the wider, wilder world we have in common. Conference participants were provoked to consider the following questions: “How might phenomenology have us recognize a primacy of movement and bring us in touch with the motions and gestures of the multiple lifeworlds of daily living? What worlds from ecology to technology privilege certain animations? What are the affects and effects of an enhanced phenomenological sensitivity? What senses, feelings, emotions and moods of self-affirmation and responsiveness to others sustain us in our daily lives? And to what extent might the descriptive, invocative, provocative language of phenomenology infuse the human sciences and engender a language for speaking directly 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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.019 | 0.021 |
| Insufficient payload (model declined to judge) | 0.014 | 0.010 |
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".