Bibliographic record
Abstract
This research document is a support paper to my MFA thesis exhibition, Seen|Unseen. I am a transmasculine/transgender digital media artist living in Canada where currently, as of 2021, there is still an unprecedented drug crisis in which it feels as though little is being accomplished to curb the losses. My younger sister became a statistic in the ‘opioid crisis’ when she died in her bedroom, the drug she was expecting being unexpectedly laced with fentanyl. Occurring within a week of this event was the date in which I had originally planned to share my trans identity to my family. I continued to develop my trans identity separate from my parents until a few months later when I told them. Being unable to share that part of myself with my sister, I was faced with what felt like insurmountable guilt and regret in my decision to wait as long as I had. I have created the artworks for this thesis exhibition to address the interlacing aspects of familial loss and transition, and what it means to create a new relationship with someone who is gone. I never had the chance to be my sister’s brother, and therefore, my only memories and experiences with her are when I was not my authentic and natural self. Inspired by ideas like active imagination in Jungian analytical psychology and expressive art therapy, I created digitally animated artworks that first addressed the grief of a sister’s death, then an intersection and acknowledgement of the idea that our lives never truly overlapped. Finally, I created artworks that address the simultaneous-with-grief excitement and euphoria of my physical transformation, and of an inherent loss and change of identity in transition. The use of digital animation is in itself an artistic process and expression of psychological well-being, creating a relationship between the artist and the artwork that aids in healing.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.242 | 0.107 |
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".