Introduction to the Social Media + Society Special Issue on Selfies: Me-diated Inter-faces
Bibliographic record
Abstract
This introduction to the special issue entitled Me-diated Inter-faces begins by bringing into question the concept of positioning: what is it that we are doing when we take a position within the study of social media? Reviewing the work of the inaugural manifestos of the journal Social Media + Society on one hand, and the introduction to the special issue on selfies for the International Journal of Communications on the other, this introduction provides both critical and creative in-roads for thinking and re-thinking digital self-images shared on social media. Given the constantly changing nature of social media, this paper is a call to researchers of social media to not fall prey to the ossification of our current positions since theorizing the “social” in social media means always at once theorizing the body. As such this intro offers numerous and diverse perspectives on the body that might inform emerging thoughts on the socially media body. The introduction then provides an overview of the papers in this special issue and concludes by offering openings and ruptures for further discussion, rather than closure of conclusions.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.060 | 0.021 |
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".