Indexical Ways of Knowing: An Inquiry Into the Indexical Sign and How to Educate for Novelty
Bibliographic record
Abstract
In this paper, I propose that the indexical sign can be used to derive a model for active (touching-and-feeling) learning. The implicit processes involved in the subtle reading of indices contain explanatory possibilities for understanding how students adapt to novelty in the learning process. Besides looking at how indexicality functions in human ontogeny and cognition, I will also examine the human capacity for modeling our world through aggregations of systems of representations (Sebeok, 1994). Modeling systems (with their implicit recognition that the human is a semiotic animal) help us to conceptualize how novelty is assimilated in the learning process. I posit that how we come to terms with new experiences (and new stimuli generally) is of an indexical nature. I am specifically referring to the site where "the new" comes from the outside (like a rain cloud signaling the coming storm) and acts upon us. We can recognize the rain cloud as an experiential pattern (as a semiotic entity) or not; the rain is still going to bear down on us regardless of the success of our interpretations. This existential realness of indexical signs is precisely their power to function as a pedagogical tool, to help us assimilate and accommodate to novel stimulus. The concept of modeling helps us conceptualize the process in which the new stimulus is absorbed and integrated into our cultural/semiotic systems. In short, this paper aims to explore what I call the indexical rub of learning; that initial friction or resistance felt when meeting a new experience. My hope is that this exploration can aid in the cultivation of a mindset in teachers, students and researchers that does not fear this resistance, but can use it to propel positive absorption (in the Deweyian sense) and engaged learning.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.032 |
| Scholarly communication | 0.006 | 0.019 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| 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".