Committed Commenting and the Virtual Visage: Contextualizing Sorority Social Media Encounters
Bibliographic record
Abstract
This article addresses the ways in which collegiate sorority women deploy sorority-specific aesthetic cues to construct socially acceptable and recognizable presentations of themselves online. I suggest that sorority members initiate and invite social media interaction as a means of parlaying their own media posts into discursive sites, thereby participating in a complex and considerably stratified economy of display and recognition. Sorority members also exert social capital through public demonstrations of social network linkages— demonstrations which can only be performed successfully if one maintains legitimacy and good standing within the media economy. I probe the implications of theorizing social media posting (particularly to the digital media platform Instagram) as a communal art creation practice that strengthens group social linkages and reifies communally observed aesthetic guidelines. I also address the stylistic and discursive regimens that shape expectations of media presentation, contrasting these practices with the comparatively candid and informal presentation styles exemplified in Fake Instagram (“finsta”) posting behaviors.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.017 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".