The Onto-Epistemologies of New Materialism: Implications for Applied Linguistics Pedagogies and Research
Bibliographic record
Abstract
Abstract The Douglas Fir Group (2016) argued that applied linguistics needed new interdisciplinary perspectives, and I suggest here that the concepts provided by new materialism might aid in gaining such perspectives. New materialism foregrounds the material nature of humans, discourses, machines, other objects, other species, and the natural environment, as well as constant change, non-binary thinking, and the porosity of boundaries; it also asks for the posing of new problems and new concepts to ‘bring forth a world distinct from what we already are’ (Colebrook and Weinstein 2017: 4). Refusing the central binaries and hierarchies of Cartesian thinking, new materialism’s relational ontology stresses becoming; people, discourses, practices, and things are continually in relation and becoming different from what they were before. New materialist conceptions of knowledge/knowing and language/languaging are also relational, processual, and entangled. I review recent new materialist educational research and present two descriptions of events in my own research to show what pedagogical and research-oriented questions might be stimulated from this perspective.
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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 | 0.027 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.006 | 0.095 |
| Scholarly communication | 0.017 | 0.028 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 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".