Intertextual semantics: A semantics for information design
Bibliographic record
Abstract
Abstract In most discussions about information and knowledge management, natural language is described as too fuzzy, ambiguous, and changing to serve as a basis for the development of large‐scale tools and systems. Instead, artificial formal languages are developed and used to represent, hopefully in an unambiguous and precise way, the information or knowledge to be managed. Intertextual semantics (IS) adopts an almost exactly opposite point of view: Natural language is the foundation on which information management tools and systems should be developed, and the usefulness of artificial formalisms used in the process lies exclusively in our ability to derive natural language from them. In this article, we introduce IS, its origins, and underlying hypotheses and principles, and argue that even if its basic principles seem remote from current trends in design, IS is actually compatible with—and complementary to—those trends, especially semiotic engineering (C.S. de Souza, 2005a ). We also hint at further possible application areas, such as interface and interaction design, and the design of concrete objects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.005 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".