Bibliographic record
Abstract
The ubiquitous digital space has seen a rise of virtual avatars which has introduced opportunities for individuals to reimagine, challenge, and explore their identities. These customisable, symbolic representations allow users to experiment with various aspects of their identity, such as gender, race, age, and appearance, marking them free from the constraints of physical reality. The anonymity provided by digital avatars empower creators to address sensitive socio-political issues without fear of reprisal, making contentious topics accessible and less confrontational, thereby fostering critical reflection among audiences. It provides a space to challenge caste and class hierarchies by exposing societal hypocrisies, often shrouded under the garb of humour and satire. Through this paper, we explore the use of satirical digital avatars on Instagram by analysing two prominent Instagram accounts: Swineryy and Anurag Minus Verma (through the character, Ronnie Malhotra) to challenge societal hypocrisies and question social hierarchies. This paper examines how satirical Instagram creators in India and Pakistan- two opposing nations unified by humor- use digital avatars as a tool of self-performance and provide social critique through mockery. Using qualitative critical discourse analysis, we identify themes of gendered satire, performative politics, religious dogmatism, and institutional critique. The paper aims to address the following pertinent questions: i) How do digital avatars on Instagram shape or reshape the presentation of self, enabling unified creators to perform fragmented or strategic identities within the socio-cultural frameworks of India and Pakistan? ii) How do satirical digital avatars employ humor, vantage anonymity, and stylized performance to critique hegemonies of caste, class, and privilege, particularly in the Indian and Pakistani contexts? iii) What role does audience engagement play in reinterpreting, amplifying, transforming, or curbing the meaning of such satirical performances, and how do these interactions contribute to the upending or upliftment of social hierarchies in digital spaces? Drawing on Goffman’s concept of identity as performance, this qualitative study explores how digital personas navigate the boundaries between personal and public critique in their everyday lives and understand how avatar-based representations influence creator agency and audience reception. Employing a critical discourse analysis framework, the paper examines how the creators address power dynamics, challenge privilege, and create space for marginalised perspectives.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.066 | 0.056 |
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".