Bibliographic record
Abstract
Abstract This interview/dialogue addresses an important issue of how educational semiotics is grounded in the history of ideas. The discussions concern the shared history of semiotics and liberal education; the modern university and its medieval antecedents; semiotic consciousness, the traces of which are found in both Christianity and Islam (and the hermeneutics of Abrahamic and mystical religions, in general); intercultural translation; the relationship between learning (conceptualized edusemiotically) and biosemiotics, and how our social understandings of learning determine and shape our basic relationship to the world. Touching on the concepts of scaffolding and evolution, the chapter discusses adaptation in relation to learning, social semiotics and contemporary social reality, while imploring us to consider education in terms of its service to learning (and not the other way around). Campbell: This interview was originally published as a recorded podcast-interview in 2017, on philosophasters.org as part of the interview series Signs of Life. Thank you to Thomas Hoeller for recording and editing the sound and music, and Marion Benkaiouche for transcribing the interview. Thank you, Inna Semetsky, for summarizing the dialogue, included in part in the above abstract description. Please bear in mind that as this interview was conducted two years ago, the author´s current ideas on some of these topics may have changed.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.050 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".