Challenges to Creative Communication: Prolegomena to Narrative Reformation
Bibliographic record
Abstract
An important element of scholarly communication (especially the core work of researchers) is the ability for scholars to communicate. When scholars can augment what their colleagues are aware of, and when skills and abilities can be understood, the opportunities for progress are enhanced. In order to communicate optimally there must be a set of linguistic tools of which researchers can avail themselves. Interdisciplinary success can be made more likely by, among things, semiotic analysis that renders signification and interpretation possible. This paper presents conceptual and practical uses of semiotics as a means to assist scholarly communication.Un important élément de la communication entre chercheurs (particulièrement celle liée au travail fondamental des chercheurs) est la capacité pour les chercheurs de communiquer. Lorsque les chercheurs peuvent accroître le savoir de leurs collègues et lorsque l'on comprend mieux les compétences et les habiletés, les occasions de progrès s'en trouvent augmentées. Pour mieux communiquer, il doit y avoir un ensemble d'outils linguistiques dont les chercheurs peuvent se servir. On peut favoriser la réussite interdisciplinaire en recourant, entre autres, à l'analyse sémiotique qui rend possibles la signification et l'interprétation. Cette communication présente des utilisations conceptuelles et pratiques de la sémiotique comme moyen d'assister la communication entre chercheurs.
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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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.073 |
| Scholarly communication | 0.026 | 0.029 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".