Introduction: Vibrant Materialities Across Media, Literature, and Theory
Bibliographic record
Abstract
This special issue of *Imaginations: Journal of Cross-Cultural Image Studies* takes materiality as the critical touchstone for a new comparative literary and media studies. We ask: How can an examination of the modes of inscription across media, platforms, and interfaces draw greater attention to what is often ignored in critical conversations about texts, objects, and bodies; how can such an investigation attend to the vitality of their materiality? Studies of materiality may occur of/in: images, texts, subjects, and objects; philosophical approaches to materiality and interrelationality; cultural considerations of user relationships to media materiality; mediums of scholarship, including critical applications in open-source publishing; critical making and research-creation; and experiences, interactions, and representations of digital spaces. Here, we take materiality to include actants, actors, inscriptions, assemblages, interfaces, objects, bodies, things and the things we think with, as well as their agencies and provocations. The papers reconsider the entanglements between inscription and representation and between inscription and world through theoretical frameworks that show that “matter matters,” using approaches that include code studies, critical making, digital humanities, ecocriticism, feminism, Marxism, media archaeology, new materialism, and phenomenology.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.019 | 0.014 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".