The Rhetoric of Twitter in Terms of the Aristotelian Appeals (Logos, Ethos, and Pathos) in ESL/EFL Educational Settings
Bibliographic record
Abstract
The article argues that the Aristotelian appeals (logos, ethos, and pathos) can be taught through the use of Twitter as an educational tool to build connections between everyday informal writing on social media and academic writing. It highlights the utilization of Twitter in English second/foreign language (ESL/EFL) educational settings for supporting learners’ rhetorical awareness and understanding of different writing genres. The main purpose of this article is to provide pedagogical implications and future research potentials on the use of Twitter in ESL/EFL educational settings. The Aristotelian appeals are discussed as the framework for the analysis of Twitter’s content in ESL/EFL educational contexts. In this regard, this research question is addressed: How can Twitter serve as a tool for teaching the fundamentals of writing competency in terms of the Aristotelian appeals (logos, ethos, and pathos) in ESL/EFL educational settings? To explore the current state of research and inform future studies, the researcher reviews selected academic articles on the use of Twitter in ESL/EFL language classes. All articles were accessed using Google Scholar, ERIC, and ProQuest databases. The researcher examines empirical studies published in peer-reviewed journals as well as non-empirical studies. This article addresses Twitter users’ constructions of logos, ethos, and pathos, and presents some of the accessible characteristics of Twitter. Also, it briefly provides pedagogical implications of understanding the Aristotelian appeals through Twitter in ESL/EFL educational contexts that can support the teaching and learning processes. Lastly, the researcher proposes potential research directions for Twitter use in ESL/EFL educational settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".