Bibliographic record
Abstract
After the linguistic and the affective turns, the new materialist and the performative turns, the cognitive and the posthuman turns, it is now time to re-turn to the ancient, yet also modern and still contemporary realization that humans are mimetic creatures. In this second installment of the Homo Mimeticus series, international scholars working in philosophy, literary theory, classics, cultural studies, sociology, political theory, and the neurosciences engage creatively with the theory developed by Nidesh Lawtoo in Homo Mimeticus: A New Theory of Imitation to further the transdisciplinary field of mimetic studies. Agonistic critical engagements with precursors like Plato, Aristotle, Nietzsche, Bataille, Irigaray and Girard, involving contributions by leading experts of imitation such as Mikkel Borch-Jacobsen, William E. Connolly, Henry Staten and Vittorio Gallese among many others, reveal the urgency to rethink mimesis beyond realism. From imitation to identification, mimicry to affective contagion, techne to simulation, mirror neurons to biomimicry, Homo Mimeticus casts a shadow—but also a light—on the present and future, from social media to the Anthropocene.
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 | 0.001 | 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 | 0.010 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".