Proto-Acting as a New Concept: Personal Mimicry and the Origins of Role Playing
Bibliographic record
Abstract
Proto-acting is introduced here as a new concept that refers to a set of processes that are intermediate between everyday role playing (in the Erving Goffman sense) and dramatic acting. Its most characteristic process is the voluntary act of personal mimicry, which can occur in everyday contexts, such as quoting someone during conversation, or in performance contexts, such as impressionism. Proto-acting involves character portrayal, but on a much simpler and more transient scale than in dramatic acting, where a person may portray a character for an extended period of time during a stage performance. For example, this might involve impersonating the characters while reading a bedtime story to a child, or children themselves portraying characters while engaging in pretend play. Other key features of proto-acting are that it tends to be driven by gesture, have minimal scripting, and involve short bouts of alternation between the self and characters. Proto-acting, as based on personal mimicry, might provide a cognitive foundation for dramatic acting in human development. Moreover, proto-acting itself might be underlain evolutionarily by the process of pantomime, which often involves intentional mimicry of the actions of other people. Hence, the proto-acting concept is able to shed light on processes relevant to cognition, development, the performing arts, and human evolution.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.022 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".