Métissage in <i>Art/Re-search (T)here:</i> Entanglements of be-longing and re-storying
Bibliographic record
Abstract
Art/Re-search (T)here is a project that includes seven re-searchers/co-conspirators from different academic fields who identify a need for, and absence of, arts-based research in their respective fields, including English Literature, Cultural Studies, Labour Studies, Education, Game Design, and Interdisciplinary Studies (Film Studies, Indigenous Studies & Social Work). The art/re-search that co-conspirators create tell stories differently; re-storying how and where research is done, and with whom, as it engages with (post)qualitative practices and posthumanism as a decolonizing research methodology. This article works through the (in)tensions of re-storying and art, the limitations of language and embodied (be)longings occurring through the virtual material-discursive. (T)here, the primary investigator of the project of Art/Re-search (T)here be-longs with co-conspirators and this is shared through métissage, a research methodology that unfolds via an affective decolonizing responsibility.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.023 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".