Composition Naturalized
Vol: 40, Issue: 1
2025
- 18Usage
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Usage18
- Abstract Views18
Article Description
The emergence of Large Language Models has exposed composition studies’ long-standing commitment to Cartesian assumptions that position writing as a nonmaterial, distinctly human activity. This paper develops a naturalized theory of composition grounded in Deweyan pragmatic naturalism that dissolves the nature/culture dualism embedded in contemporary theory and practice. We advance an eco-ontological account that understands compositional activity as emerging from within the matrix of animal behavior and introduce “compositional viability” to theorize how writing functions as a biosemiotic tool for environmental reconstruction. This framework yields three pedagogical implications: attending to somaesthetics, cultivating writerly habits, and orienting composition toward viable action.
Bibliographic Details
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