Instagram and female celebrity: exploring gender performances through social visual semiotics, feminist theory and framing analysis
Bibliographic record
Abstract
With the advent of social media, and specifically Instagram, individuals now have the ability to instantly communicate their moods, thoughts, and ideas through personally created visual messages. Instagram is designed to communicate ideas through self-generated digital images uploaded to the platform and network in real time from mobile devices, especially smartphones. This has had an impact on what is accepted as important media content, with fans, publics, and popular culture having a seemingly insatiable appetite for the personal details proffered by celebrities’ online profiles (Marwick & Boyd, 2011). These images are redefining our understanding of celebrity, reformatting fans’ expectations of reality, and helping to define emerging modes of subjectivity in a world dominated and increasingly defined by social media networks. This Masters of Professional Communication Major Research Project examines how social media networks are changing the way female celebrities portray themselves to the public and how these digital platforms are assisting in the cultivation of celebrity personas. Informed by social visual semiotics, postmodern feminist theory, and framing analysis this research project will analyze: 1) the Instagram accounts of three prominent female celebrities: Ellie Goulding, Kat Dennings, and Beyonce; 2) how these celebrities’ self-portrayal assists in their ability to create an accessible persona for their fans; and 3) what these prominent female celebrities’ social media performances reveal about female empowerment and self-representation in our social media saturated era.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".