Bibliographic record
Abstract
This article describes how a class dedicated to supporting language needs in concurrent science courses developed complex grammatical literacy with an emphasis on a functional understanding of language for technicality, precision, and persuasion. The success of the curriculum was predicated on three factors: the grammatical content selection and sequencing, activity design, and assessment design, all motivated by the needs of the students in their concurrent courses. Specific content was chosen, including transitivity and clause forms, the systemic functional grammatical concepts of logic, circumstances, and grammatical metaphor, and the Appraisal notion of engagement. By moving from forms of language to functions and meanings, students built the skills to negotiate complex grammatical structures in textbooks and word problems and therefore access the scientific knowledge. Regular group work and discussion focused on texts that students brought from their science classes, allowing students to slowly build familiarity with concepts in a way that emphasizes scientific understanding over grammatical perfection. The assignments included group analyses, a short conversation with the instructor on an individual analysis, and both individual and pair written assignments where sources were rewritten with strategic language use to paraphrase and position sources, claims, and information. While achievement levels varied, students anecdotally reported being able to read more quickly, identify key information under exam conditions, and apply the knowledge learned in new courses and assignments. Cet article décrit comment un cours dédié au soutien des besoins linguistiques dans des cours de sciences concomitants a développé une littératie grammaticale complexe qui met l’accent sur une compréhension fonctionnelle de la langue pour la technicité, la précision et la persuasion. Le succès du programme d’études repose sur trois facteurs : la sélection et l’enchaînement du contenu grammatical, la conception des activités et la conception de l’évaluation, tous motivés par les besoins des apprenants dans le cadre de leurs cours concomitants. Un contenu spécifique a été choisi, notamment la transitivité et les types de propositions, les concepts de la grammaire systémique fonctionnelle, comme la logique, les circonstances et la métaphore grammaticale, ainsi que le concept de l’évaluation de l’engagement. En passant des formes linguistiques aux fonctions et aux sens, les étudiants ont acquis les compétences nécessaires pour négocier les structures grammaticales complexes des manuels et les défis lexicaux complexes et ainsi accéder aux connaissances scientifiques. Les travaux de groupe et les discussions régulières ont porté sur des textes que les étudiants avaient apportés de leurs cours de sciences, ce qui leur a permis de se familiariser progressivement avec les concepts d’une manière qui met l’accent sur la compréhension scientifique plutôt que sur la perfection grammaticale. Les devoirs comprenaient des analyses de groupe, une courte conversation avec l’enseignant basée sur une analyse individuelle, et des devoirs écrits individuellement et en binôme où des sources étaient réécrites avec une utilisation stratégique de la langue pour paraphraser et positionner les sources, les affirmations et les informations. Bien que les niveaux de réussite aient varié, les étudiants ont déclaré être capables de lire plus rapidement, d’identifier les informations clés en contexte d’examen et d’appliquer les connaissances acquises dans de nouveaux cours et de nouveaux devoirs.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.054 | 0.024 |
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".