Extension of the type/token distinction to document structure
Bibliographic record
Abstract
The type/token distinction introduced by C. S. Peirce and taken up by many others is familiar when applied to individual symbols or characters in a writing system, and also when applied at a higher level to words (and word-like objects). Some writers apply the distinction not only at some basic or foundational level but also as a description of higher levels of organization. This paper follows their example by outlining a concrete extension of the type/token distinction to all levels of document organization, specifying that higher-level types may contain sequences of lower-level types, and similarly for higher- and lower-level tokens. We further extend the usual model of types and tokens by allowing higher-level types to contain not just sequences of (lower-level) types but also sets, bags, conjunctions and disjunctions of types. This allows the system to deal gracefully both with indeterminate documents (e.g., a manuscript in which it is not clear whether a given mark on the page represents a 'c' or a 't') and with intentionally polyvalent documents, in which some marks are to be read as tokens of more than one type, as in the “ambigram”, a sort of combination puzzle and calligraphic artwork in which the shapes on the page may be read in different ways, or the same way, in different directions. This account of document structure in terms of types and tokens is similar in many ways to that offered by SGML, XML, and other systems of descriptive markup. On this view, SGML and XML elements are, strictly speaking, types (and tokens) in Peirce's sense of those words. Some techniques developed in other areas to which the type/token distinction is relevant may be useful in work on markup languages (and vice versa).
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.008 | 0.019 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".