On the Pragmatics and Problematics of Defining Beauty and Character: The Greek Poet Lucian (120-200) Engages Exacting Portraitures and Difficult Subjects
Bibliographic record
Abstract
Although best known as a satirist of the classical Roman era, Lucian's (c120-200CE) Essays in Portraiture and Essays in Portraiture Defended provide considerable insight into the problematics of people knowing and defining objects (along with the consequential and related matter of people sharing their definitions of reality with others). Engaging notions of admiration, beauty, and character in these two statements, Lucian not only faces the task of establishing viable frames of reference for linguistically defining the essence of a woman deemed to be particularly beautiful and gracious but also assumes the challenge of defending one’s preferred definitions of particular subject matters from others who do not share these views. Whereas Lucian uses the works of prominent sculptors, painters, poets, and philosophers as reference points in articulating beauty and grace, this paper also acknowledges the perils of people who sincerely express their viewpoints on others even when these descriptions of others are cast in clearly positive terms. Lucian may be a lesser-known classical Greek (Syrian) author, but he is an astute observer of human endeavor. Lucian’s work on portraiture also has a striking cross-cultural and transhistorical relevance for a more enduring pragmatist emphasis on human knowing and acting. Not only is Lucian (a) explicitly attentive to the necessity of people establishing frames of reference for describing objects to others in meaningful terms, but he also overtly recognizes (b) the multiple viewpoints that people may invoke with respect to describing particular objects, (c) the resistances that people may encounter from others, and (d) the importance of speakers articulating the foundations for their claims amidst contested notions of reality. Approached from an interactionist perspective (Mead 1934; Blumer 1969; Strauss 1993; Prus 1996, 1997, 1999), wherein attention is given to the more general matters of people acquiring perspectives, defining objects, and sustaining particular notions of reality, this paper uses Lucian’s materials on portraiture as a cross-cultural and transhistorical resource both for assessing (and qualifying) existing interactionist conceptualizations of human group life and for suggesting some more particular areas of inquiry to which contemporary scholars may attend.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.048 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".