Pomp and Circumstances: From Research, in Practice, for Students
Bibliographic record
Abstract
Circumstances are powerful grammatical tools for representing precision and specificity in the clause, and they add contextual factors necessary for nuanced disciplinary discourse. This type of detail expressed through circumstances is significant in academic discourse, yet it is often neglected in favor of attention to participants and processes. Drawing on our experiences teaching embedded, discipline-specific EAP to multilingual students at Vantage College at the University of British Columbia, we demonstrate how circumstances, and more broadly circumstantiation, generate crucial insights for both comprehension of complex academic materials and generation of student texts. We also show how circumstances are useful grammatical concepts for working across the metafunctions and ranks in multiple ways: transitivity analyses of circumstance types within disciplinary texts, for example, highlight the relationship between their deployment and genre and stage variations. Probe questions provide a pedagogical lens to see detail and context in critical reading and writing. Circumstances support organization by building Theme patterns through the strategic use of marked topical Themes, and convey interpersonal evaluation in academically appropriate ways. Finally, connecting circumstances to broader circumstantial meanings provides a powerful mechanism for paraphrasing with grammatical metaphor. All through our analyses and materials, we illustrate how explicit instruction in circumstances adds to students’ linguistic and analytical repertoires and facilitates powerful insight for how circumstantial information contributes to academic discourse and knowledge-building.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".