Bibliographic record
Abstract
The close relationship between motion (bodily movement) and emotion (feelings) is not an etymological coincidence. While moving ourselves, we move others; in observing others move – we are moved ourselves. The fundamentally interpersonal nature of mind and language has recently received due attention, but the key role of (e)motion in this context has remained something of a blind spot. The present book rectifies this gap by gathering contributions from leading philosophers, psychologists and linguists working in the area. Framed by an introducing prologue and a summarizing epilogue (written by Colwyn Trevarthen, who brought the phenomenological notion of intersubjectivity to a wider audience some 30 years ago) the volume elaborates a dynamical, active view of emotion, along with an affect-laden view of motion – and explores their significance for consciousness, intersubjectivity, and language. As such, it contributes to the emerging interdisciplinary field of mind science , transcending hitherto dominant computationalist and cognitivist approaches. As of February 2018, this e-book is freely available, thanks to the support of libraries working with Knowledge Unlatched.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".