Processes of Imaging and Imagining: Toward a Pragmatic Clarification of the Image
Bibliographic record
Abstract
The paper aims at a pragmatist clarification of imaging and imagining. Using Peirce’s doctrine of the three grades of conceptual clarification (tacit familiarity, abstract definition, and pragmatic elucidation), the author tries to clarify our processes and practices of imaging and imagining by considering them in light of these distinct levels. Above all, he endeavors to push the discussion from the level of abstract definition to that of pragmatic clarification, thereby focusing on the habits of agents in situ, not simply verbal formulations offered in the abstract. Borrowing from Barbara Bolt, he uses as one of his examples that of an artist “working hot”. A process wherein conscious intentions and unconscious drives, cognitive designs and thick materiality, conspire to embody themselves in perceptible media is one especially worthy of examination. Hence, from an overview of Peirce’s general theory of signs, the author turns to some of the specific uses of that theory, uses not central to Peirce’s own interests. Artistic production and performance are foremost among these uses. These are, after all, ones in which the dynamics of imaging and imagining are often more vividly on display than elsewhere.
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 | 0.021 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.077 |
| Scholarly communication | 0.014 | 0.032 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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".