Bibliographic record
Abstract
This original and interdisciplinary volume explores the contemporary semiotic dimensions of the face from both scientific and sociocultural perspectives, putting forward several traditions, aspects, and signs of the human utopia of creating a hybrid face. The book semiotically delves into the multifaceted realm of the digital face, exploring its biological and social functions, the concept of masks, the impact of COVID-19, AI systems, digital portraiture, symbolic faces in films, viral communication, alien depictions, personhood in video games, online intimacy, and digital memorials. The human face is increasingly living a life that is not only that of the biological body but also that of its digital avatar, spread through a myriad of new channels and transformable through filters, post-productions, digital cosmetics, all the way to the creation of deepfakes. The digital face expresses new and largely unknown meanings, which this book explores and analyzes through an interdisciplinary but systematic approach. The volume will interest researchers, scholars, and advanced students who are interested in digital humanities, communication studies, semiotics, visual studies, visual anthropology, cultural studies, and, broadly speaking, innovative approaches about the meaning of the face in present-day digital societies.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.053 | 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".