Bibliographic record
Abstract
Abstract Our faces play essential roles in defining us as individuals. They are the most immediately identifiable parts of our bodies. We use our faces to communicate emotions and to interact socially. Sometimes, despite our intentions, our faces reveal our thoughts even when we do not speak. In several medical conditions, the facial aspect confirms diagnosis, and while surgical alteration of craniofacial anomalies can do much to normalize appearance, patients are always confronted with the question of what is normal, and with the fact that beauty itself may be nothing more than a culturally determined concept. This book explores a range of distinct yet related perspectives on the face--the evolutionary, the developmental, the anatomic, the dysmorphic and genetic, the surgical, the psychological; the sociocultural, and the artistic. As a cross-disciplinary study, it is the first to comprehensively address the question of what constitutes a face, and to span the gap between symbolic interpretation and scientific fact. Both broadly informative and in-depth in its discussions, this highly readable book will be of interest to biologists, geneticists, plastic surgeons, craniofacial surgeons, oral and maxillofacial surgeons, psychologists, sociologists, anthropologists, and others with a special interest in the face.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".