Leveraging Multimodal Onto-Epistemology in Pedagogical, Curricular, and Research Contexts
Bibliographic record
Abstract
The concepts of ‘multimodality' and ‘multiliteracies' are sometimes used interchangeably in education discourses. In this chapter, multimodality is reconceptualized, not as a newfangled idea for enriching the literacy competencies of students, but rather as an onto-epistemology without which our meaning-making processes would always run the risk of insufficiency and inadequacy in curricular, research, and pedagogical contexts (Abbas, 2023). The basic premise is that reality, human ontology, and our perceptions of both, are inherently, irrevocably, and ineluctably multimodal (Abbas, 2023). Additionally, the pedagogical, curricular and research implications of a multimodal onto-epistemology or movement-thinking (Abbas, 2023) are discussed. Without this reconceptualization, reaching a socially shared understanding of a culturally and historically responsive curriculum, and a just world, may both remain elusive despite the best of intentions on the part of the multiple stakeholders. As such, multimodality is presented as a framing context for all curricular and pedagogical discourses.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".