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Record W2808365028 · doi:10.24382/870

Exploring Privilege through the creation and Live/Digital Performance of an Alter Ego

2018· dissertation· en· W2808365028 on OpenAlexfundno aff
Gemma Nicole Chatwin

Bibliographic record

VenuePEARL (University of Plymouth) · 2018
Typedissertation
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsnot available
FundersUniversity of Windsor
KeywordsPrivilege (computing)Id, ego and super-egoPsychologySocial psychologySociologyComputer scienceComputer security

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.201
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.042
GPT teacher head0.252
Teacher spread0.211 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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