THE PRAXES OF SUBJECTIVITIES IN THE ACADEMY: INSTITUTIONAL DEMOLECTS VS. INDIVIDUAL IDIOLECTS
Bibliographic record
Abstract
Price (2001) evokes the constraints of social contexts on language use by this quote from P. L. Berger and T. Luckmann (1967) thus: “I encounter language as a facility external to myself and it is coercive in its effect on me. Language forces me into patterns”. The dissemination of knowledge through established conventions of academic discourse seemingly demonstrates the capacity of the discourse to effectuate the learning and expression of such knowledge (Hyland and Hamp-Lyons 2002). This crucial proficiency therefore means specific practices in academic contexts and communicative behaviours. Academic literacy thus applies to a complex set of skills to which allude Dudley-Evans and St. John (1988), and to a “common core of universal skills or language forms ” (Hutchison and Walters 1998; Spack 1988). Inescapably, critical questions arise to wit: Does a Language for Academic Purposes (LAP) exist to delineate disciplines? Is its specificity defensible in heterogeneous academic communities? How inherently different are individual discourse communities and disciplines vis-à-vis their social, communicative and cognitive dimensions?
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.079 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".