The rhetorical dimension of images: identity building and management on social networks
Bibliographic record
Abstract
Abstract This article proposes a semio-rhetorical epistemology for visual documents, one capable of accounting for both their internal configuration, which we shall call the compositional dimension, and their persuasive force within public space, or their rhetorical dimension. The field of reference will be that of identity-related images on social networks, because compared to other kinds of images, such as artistic or professional ones, they adopt new compositional solutions and new dynamics of circulation. To test this theoretical framework, we will conduct an analysis which has never been carried out in semiotics and which, as far as we know, remains very rare even in the overall field of visual studies, that is, the analysis of the profile of an Instagram influencer’s visual production, that of Canadian artist Rupi Kaur. Taking into account the flow of images shared over time, we will focus primarily on the compositional dimension that articulates the specificity of the language of images. The most appropriate model for investigating social network photos seems to be that of the portrait, thanks to which we will identify a first series of regularities and deviations. Secondly, we will turn towards the rhetorical dimension – the persuasive strategies found within, through, and towards images – focusing on the analysis of a single photo: on the one hand, it is a shot which presents greater compositional richness than others; on the other hand, it has greatly impacted the notoriety of the influencer, due to the censorship incurred on Instagram, its abundant coverage by traditional media, and the heated debate it triggered on social media. We will thus propose a reinterpretation of Paul Ricœur’s theory of identity in order to balance the rhetorical and the compositional dimensions through a unitary theoretical hypothesis. Visual identity on social networks is always the result of a negotiation between two opposite tendencies: on the one hand, the experiential pressure expressed through images related to the body and everyday practices; on the other, the algorithmic pressure due to the delegation of the management of identity to software. The case of Rupi Kaur is a rare example of a critical mediation between these two pressures and helps us build a methodology for the examination of images belonging to other social domains.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| 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".