Re-enacting/mediating/activating: Towards a collaborative feminist approach to research-creation
Bibliographic record
Abstract
Worldwide interest in understanding art and creative practices as valid forms of knowledge production has led to the establishment of research-creation as an interdisciplinary academic field in the last twenty years in Canada as elsewhere. Its establishment relates to a growing interest in critical making and technological innovation and to the legacies of feminism(s) and its critique of the power dynamics of knowledge production within academia. This article outlines a series of interactive projects that bring visibility to Latin American women in art, science and technology and speculates on the legacies of feminism(s) in the emergence of research-creation. The four projects discussed build on each other and explore re-mediation, re-activation and re-enactment as part of a collaborative feminist research-creation methodology. I theorize their potential to activate political memory by highlighting how these three approaches to creation share a preoccupation for revisiting the past through repetition, iteration and the facilitation of intergenerational encounters among humans, non-humans and across media and technologies. While discussing the feminist orientation of these approaches, I suggest a critique of dominant modes of knowledge production that have obscured the contributions of Latin American women and offer four research-creation interventions in the media arts archive.
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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 | 0.061 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.017 | 0.111 |
| Scholarly communication | 0.023 | 0.016 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".