Shifting Spaces Outside of Time: Constructing Counter-Narratives Through Collage
Bibliographic record
Abstract
This paper explores how the methodology of collage refuses notions of linear temporality through the production of counter-narratives. As a framework for analysis, counter-narrative becomes a useful lens to understand the types of critiques that collage can engender by examining the power and privilege that art history, mass media, and constructions of identity hold in a multiracial world. By focusing specifically on the potential of collage to visualize non-linear approaches to piecing the world together through objects of material culture, this research fills a gap between collage as practice, social critique, and pedagogical methodology by weaving these notionstogether to expose a dynamic interplay between storytelling, history and pedagogy. Engaging the multidisciplinary artistic practices of three emerging contemporary artists working with collage in Canada, Jasmine Cardenas, Aaron Jones, and Anna Binta Diallo provide generative insight to challenge monolithic visual culture to reconcile histories of erasure, oppression, and colonization. Questioning and reconstructing these notions through the production of counter-narratives become a crucial method of resistance to situate radical imaginings in their histories. Ultimately, their work with collage is a direct assurance that another world is possible.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.036 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".