Grammar-and-Interlocution: English Articles as Markers of Recipient Role
Bibliographic record
Abstract
My core hypothesis is that the article system is directly motivated by a universal communicative problem, which is the necessity of mutual understanding. In the first place I argue, following Gardiner 1932, that a word does not mean per se and that a referent (Gardiner's "thing-meant") can only emerge from the agreement reached by the interlocutors in the referring process. I then suggest that articles play a key role in the process by which referents come to be shared. Their primary function is to determine the interlocutive framework within which the validating process can be achieved. Articles are thus defined as being basically markers of the role assigned to the recipient (β) in the referring process. Detailed examination of contextualized uses supports my analysis. To conclude I suggest that the distinction between different ways of reaching self and other agreement does not only structure the article system but the whole internal organization of the English language.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".