Promoting ESL Students’ Critical Thinking Skills Through a Transitivity Analysis of Authentic Materials
Bibliographic record
Abstract
Using authentic materials in the English as a second language (ESL) classroom can develop students’ critical thinking (CT) skills, expose them to more realistic English, and support their motivation. Carrying out text analyses of authentic materials in the ESL classroom can also help students become more critical in their approach to reading. Grounded in systemic functional linguistics (SFL) concepts, this paper puts forward a series of text analysis tasks, so ESL teachers can introduce their adult students to the concepts of transitivity and intentionality found in opposing newspaper articles dealing with conflict. Overall, these analyses show how the active voice can highlight the semantic value of intentionality via material processes, and how writers use strategies such as passivization and fronting of items in clauses to emphasize the responsibility for wrongdoing when reporting news. The analyses also show that context is important in determining degrees of intentionality, and intentionality can be attributed to the material processes of human actors portrayed as non-human actors. Understanding these concepts can help adult ESL students become better critical readers/thinkers. Utiliser des matériaux authentiques dans la classe d’anglais langue seconde (ALS) peut développer la pensée critique (PC) des étudiants et les exposer à un anglais plus réaliste et soutenir leur motivation. Effectuer des analyses de textes de matériaux authentiques dans la classe d’ALS peut aussi aider les étudiants à devenir plus critiques dans leur approche de la lecture. Enraciné dans les concepts de la linguistique systémique fonctionnelle (LSF), cet article présente une série d’exercices d’analyse de texte, de façon à ce que les enseignants d’ALS puissent présenter à leurs étudiants adultes les concepts de transitivité et d’intentionnalité qu’on retrouve dans des articles de presse contradictoires qui traitent du conflit. Dans l’ensemble, ces analyses montrent comment l’utilisation de la voix active peut mettre en lumière la valeur sémantique de l’intentionnalité par l’entremise de processus matériels, ainsi que la façon dont les écrivains utilisent des stratégies comme la passivisation et la mise en avant d’éléments dans les propositions pour accentuer la responsabilité d’actes répréhensibles quand ils font un reportage. Les analyses montrent également que le contexte est important pour déterminer les degrés d’intentionnalité et que celle-ci peut être attribuée aux processus matériels des acteurs humains dépeints comme des acteurs non-humains. Comprendre ces concepts aide les étudiants adultes d’ALS à devenir de meilleurs lecteurs ou penseurs critiques.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".