A Diachronic Approach to the Motive of Crypto-Functions of Formal Markers in English
Bibliographic record
Abstract
This paper, based on the exploration of crypto-functions of formal markers in English (Dong, 2016), preliminarily attempts to explicate the motive of their crypto-functions in systemic functional framework through a diachronic approach. The study firstly claims that formal markers can be treated as one of multiple linguistic expressions deployed for constructing the experiential phenomenon. Then the study assumes that the motive of crypto-functions of formal markers can be revealed from the diachronic conventionalization of multiple linguistic expressions towards formal markers in their process of experience construction. And such an assumption has then been tested with the distribution frequencies of formal markers in Corpus of Historical American English (COHA). It is found that, with the diachronic increase of the distribution frequencies of formal markers in COHA, the diachronic conventionalization of multiple linguistic expressions towards formal markers does exist, and that such a conventionalization is then attributed to language chunk composed of formal markers together with other following elements and renders formal markers prefabricate potential, thus resulting in their crypto-functions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.012 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".