Exploring Privilege through the creation and Live/Digital Performance of an Alter Ego
Bibliographic record
Abstract
Through the creation of my own alter ego, called Gemella, this Practice-as-Research project interrogates the relationship between ‘self’ and ‘ego’, and how this relationship is performed on social media sites such as YouTube, Instagram and Twitter. Using psychoanalytic theory, it considers how concepts such as the ‘shadow’ and ‘mask’ can work alongside creative performance strategies. Grounded in the experiences of my teenage years living in an expat community in Ho Chi Minh City, Vietnam, the thesis explores concepts such as ‘privilege’ and ‘cosmopolitanism’ – underpinned by scholars such as Jen Harvie, Urlich Beck and Peggy McIntosh – and how these can be negotiated to create ‘resistant’ performance. It pays particular attention to the initial stages of creating an alter ego with external presentation and costuming playing a major role. Considering the costuming choices of well-known alter egos such as Tammy WhyNot (Lois Weaver) and Miranda Sings (Colleen Ballinger), it can be noted how costumes can aid both personal and aesthetic transformation as a ‘symbolic signifier’. Moreover, the way an alter ego operates on a ‘continuum’ from acting to non-acting is explored further through digital presence. The thesis includes reflection on and documentation of formative practice research including photography, vlogs, google chats and gifs. The thesis will also include a summative live/digital performance outcome called Gemella Live: Stream 2.0.
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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.011 | 0.037 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".