Word Blending in Formal Languages*
Bibliographic record
Abstract
In this paper we define and investigate a binary word operation that formalizes an experimentally observed outcome of DNA computations, performed to generate a small gene library, and implemented using a DNA recombination technique called Cross-pairing Polymerase Chain Reaction (XPCR). The word ble nding between two words αwγ1 and γ2wβ that share a non-empty overlap w, results in αwβ. Interestingly, this phenomenon has been observed independently in linguistics, under the name “blend word” or “portmanteau”, and is responsible for the creation of words in the English language such as smog (smoke + fog), labradoodle (labrador + poodle), and Brangelina (Brad + Angelina). Technically, word blending is related to the binary word operation Latin product, the crossover operation, and simple splicing. We study closure properties of the families in the Chomsky hierarchy under word blending, language equations involving this operation, and its descriptional state complexity when applied to regular languages. We also define iterated word blending and show that, for a given alphabet, there are finitely many languages that can be obtained from an initial language by iterated word blending.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".