Constructing Inclusive Masculinities
Bibliographic record
Abstract
In an effort to counter orthodox masculinity, my work uses ornamentation \nand the insertion of my own queer body into a hyper masculine context, which \nwill expand existing notions of masculinity, into more inclusive ones. I will discuss \nissues of acceptance and visibility that affect queer athletes and the greater \nimplications. My thesis exhibition will be completed in two parts: a live \nperformance art piece and a gallery exhibition of sculpture and photography. The \nartwork has an autobiographical point of departure informed by my own family's \npersonal mythology surrounding our Scandinavian heritage, while also reflecting \non my identity, as a queer, lower class person with a rural Saskatchewan \nupbringing. I use artwork as a tool to create discussion and dialogue, which \nforwards my own agenda of creating more inclusive versions of masculinity. This \nis significant when looking at the social atmosphere facing queers in North \nAmerica.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.015 | 0.037 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".