Friction-free authenticity: mobile social networks and transactional affordances
Bibliographic record
Abstract
This paper contextualizes and critically examines the incorporation of transactional features into two popular mobile social media apps: Instagram and Snapchat. It examines how mobile social media acts as an interface between culture and commerce. We situate this interface within a larger political economic context in which tech companies are embracing ‘fintech’ to drive growth. We argue that mobile social media platforms play a unique role in monetising personal data and context awareness through their development of ‘transactional affordance’ – a term we develop to understand new features allowing users to connect content to forms of payment. We argue that the success of these affordances is tied to labour associated with the ‘performative authenticity’ of social-media influencers. Our first case study examines the recent development of ‘shopping’ and ‘checkout’ features on Instagram, and the significance of this feature for the economic growth of parent company Facebook. We then look at how the specific development of augmented reality features on Snapchat serve as the basis for new transactional affordances in everyday contexts. We conclude the paper by arguing that the contextual commerce these phenomena entail signals a shift to a transactional culture in which everyday interactions become opportunities for consumption.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.009 | 0.021 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".