Bibliographic record
Abstract
Abstract. Whereas type‐logical grammars treat syntactic derivations as logical proofs, usually represented by two‐dimensional diagrams, I here wish to defend the view that people process linguistic information by one‐dimensional calculations and will explore an algebraic approach based on the notion of a ‘‘pregroup,’’ a partially ordered monoid in which each element has both a left and a right ‘‘adjoint.’’ As a first approximation, say to English, one assigns to each word one or more ‘‘syntactic types,’’ elements of the free pregroup generated by a partially ordered set of ‘‘basic types,’’ in the expectation that the grammaticality of a string of words can be checked by a calculation on the corresponding types. This theoretical framework provides a simple foundation for a kind of feature checking that may be of general interest. According to G. A. Miller, there is a limit to the temporary storage capacity of our short‐term memory, which cannot hold more than seven (plus or minus two) ‘‘chunks’’ of information at any time. I explore here the possibility of identifying these chunks with ‘‘simple types,’’ which are obtained from basic types by forming iterated adjoints. In a more speculative vein, I attempt to find out how so‐called constraints on transformations can be framed in the present algebraic context.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".