Bibliographic record
Abstract
Contexts of culture and situation All discourse is produced in context and interpretation depends on contexts of production and interpretation being, in some measure, shared. Early ethnographic work addressed context dependency by positing contexts of culture and context of situation (Malinowski 1923; 1935; Firth 1957). Context of culture accounted for sets of culturally specific beliefs, expectations and practices in terms of which people interpret events around them. Context of situation referred to patterns of behaviour and talk which appear so regularly in association with a particular activity that they are understood as (abstract characterizations of) the function of the situation type. Behaviours which do not reflect some expected pattern can be interpreted as irrelevant, and behaviours which appear totally unrelated to the contexts in which they occur may be judged uninterpretable. Later work by Halliday, Hasan, Gregory, Martin and others refined and developed ideas of context. Our view is once again a hybrid, informed by Halliday's ethnographic perspective (e.g. 1977; 1978; 1984; 1994), by our awareness that contexts are significantly matters of what speakers know (e.g. van Dijk 1977; 2006; van Dijk and Kintsch 1983; Gregory 1988), and by work in AI, psychology and discourse analysis on top-down cognitive models as to what ‘contextual knowledge’ might be like. The latter approaches (and ours) differ from traditional functionalist and ethnographic approaches in explicitly situating context in neurocognitive domains of semantic and episodic memory (see also van Dijk 2006).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".