Figuring the Topos: Finding Common Ground in Cognitive Environments
Bibliographic record
Abstract
ABSTRACT Effective communication relies on the use of rhetorical devices and strategies to make ideas present in the minds of an audience. By employing the concept of cognitive environments, we can use the visual analogy of making an idea “present” to its fullest effect, empowering our rhetorical skills and helping influence audience reception. In this article, the author argues that while cognitive environments do indeed provide a significant and important conceptual tool for understanding and anticipating an audience’s experiences, beliefs, and knowledge, a more robust sense of agreement is necessary. The article proposes the concept of a topos that serves as a shared meeting place within cognitive environments within which both author and audience contribute their background assumptions to find common ground and commonalities in interpretations. It is in figuring the topos effectively that cognitive environments can be more accurately and effectively mapped onto each other, and breaches between such environments can be productively bridged.
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.008 | 0.040 |
| Scholarly communication | 0.015 | 0.022 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.003 |
| 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".