Face Value: Exploring the Inter-subjectivity of Mixed-Race Identity Through the Works of Jordan Clarke, Erika DeFreitas and Olivia McGilchrist
Bibliographic record
Abstract
The central premise of this thesis exhibition project addresses the notion of inter-subjectivity as it pertains to the self-portraiture of three mixed-race artists of Afro-Caribbean and Euro-white heritage: Jordan Clarke, Erika DeFreitas and Olivia McGilchrist. The project explores this inter-subjectivity by troubling the black/white race binary within the specificities of the artists’ geographies and the curated work. It also assesses the positionalities of the Toronto audience’s mixed reception of the work. The scope of the project encompasses the exhibition Face Value; a self- published exhibition catalogue; a reflective curatorial essay; an online community outreach resource; an artist panel produced in partnership with The State of Blackness conference; an onsite curator’s talk; and an exhibition report that details the process, methodology, and key learnings, contextualizes the work of the artists, and incorporates a literature review of the primary theoretical concepts that inform the project.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.024 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| 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".