How Can “The Play of Signs and The Signs of Play” Become an Attractive Model for Dealing with Eidetic and Empirical Research?
Bibliographic record
Abstract
The title of this presentation encompasses three issues: (1) an enigmatic theme (the play of signs and signs of play); (2) a model of doing something, such as unraveling a puzzle; and (3) a methodology dealing with a probable case.Considering that the order of analysis runs in the opposite direction to the order of experience, my first task is to reverse the title.Then, its three parts become: (1) an eidetic and empirical conjunction that implies a taste for evidence; (2) a rigorous model of analysis that implies a relationship between ontology (what I know) and epistemology (how I know); and (3) a case that brings an enigmatic theme.My title, based in the theme of our 42nd Annual Meeting, provided an experience peculiar to a non-native English speaker: how to interpret and use the word "play".I really felt like I was a living exemplar of one of Professor Lanigan's favorite examples: what dictionaries and encyclopedias say or do not say.Lanigan said that "dictionaries tell you how to use the words (forms, ideas) but not what word to use" and "encyclopedias tell you what facts to use (structure, experience), but not how to use them" (1992: 208-209).That was my situation with the word "play".Webster's dictionary gave me around fifty different meanings for the word "play".Which one should I choose?I am sure that I made the best choice, as I will demonstrate throughout this presentation, organized around these three ideas that in an inverse order would be theory, method, and case. Eidetic and Empirical Theory: A Taste for EvidenceThe relation between eidetic and empirical theory brings forth the old problem of how to move from idea to fact or from fact to idea; and therefore, we
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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 | 0.050 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.009 | 0.115 |
| Scholarly communication | 0.027 | 0.054 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".