Bibliographic record
Abstract
Through My Lens is a participatory performance collaboratively developed by writer/performer Amy Amantea, media designer and operator Nico Dicecco, and writer/director James Long. This piece invites audience members to describe photos to Amy, who, besides being the performer, is also the photographer behind the images. Amy is also blind, having a total of 2 percent vision in one eye. This article incorporates written reflections, excerpts from a post-tour conversation, and text from one of the performances to explore three significant moments of interaction during the creation and staging of the piece: the first between Amy and the audience, the second between Amy and Nico as they developed a digital representation of Amy’s perception of sight, and the third between Amy and director and co-writer James, who continue to engage in ongoing discussions about the work’s function. At the centre of each exchange is a reflection on the power dynamics inherent in creating and performing a piece that equally prioritized the values of accessibility and exchange. Plain Language Abstract (adapted by Kelsie Acton with Daniel Foulds) Through My Lens is a performance made by three people: Amy Amantea, who wrote and performed Nico Dicecco, who made the pictures in the performance James Long, who wrote and directed In this show, people from the audience are asked to describe photos to Amy. Amy is the person who took the photos. Amy is also legally blind with only some sight in one eye. This article is about how power is shared in a show that values access and the sharing of ideas. It looks at three moments to explore this: between Amy and the audience during the show between Amy and Nico as they worked in rehearsal to create a digital version of how Amy sees the world between Amy and James who talk about what the show should mean and do
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.019 | 0.014 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.136 | 0.049 |
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".