Systemic Functional Linguistics as appliable linguistics: social accountability and critical approaches
Bibliographic record
Abstract
This article is concerned with the relationship between Systemic Functional Linguistics (SFL) and Critical Discourse Analysis (CDA), and with SFL as a resource for socially accountable academic work. First it locates SFL within the general category of appliable linguistics (as opposed to either theoretical or applied linguistics), an approach to the study of language that is also designed to be socially accountable. Then, against the background of SFL, it traces the development first of Critical Linguistics and then of CDA, also identifying other influences incorporated within these traditions. Next, it compares CDA with other orientations within discourse analysis from the perspective of SFL, and proposes the notion of appliable discourse analysis (ADA). This leads to an overview of the dimensions of ADA, and finally to the question of the place of ADA within a general appliable linguistics.
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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 | 0.027 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.006 | 0.077 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| 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".