Bibliographic record
Abstract
Abstract This paper examines how the Peircean category of Firstness can illuminate pre-cognitive and pre-interpretative aspects of learning. This study can be understood as part of a broader edusemiotic project currently gaining momentum (cf. Semetsky (ed.) 2010, 2017; Stables and Semetsky 2014; Olteanu 2015). I explore various iterations of Peirce’s thought, from his early Lowell Lectures (1866) to what Strand (2013) has called his “rhetorical turn” following the introduction of the concept of semiosis in 1883. My contention is that engagement in arts-based processes is educationally useful in inducing and cultivating reflection on those primary aspects of consciousness that are often neglected by formal educational programs. My aim here is to explore what stimulates engaged absorption and examine how this can be applied to form an “education of inquiry” informed by Peirce’s pragmatism, which places contemplation on this pre-interpretative realm of meaning in a central role. In conclusion, the paper will show how an understanding of Firstness is necessary for understanding Peirce’s aesthetics, and thus his ethics, which depends upon the “habits of feeling” emerging from Firstness. Thus, we can understand how the cultivation of a “pedagogy of Firstness” is foundationally an ethical educational program.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.007 |
| 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 | 0.019 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".