The Marketplace of Existential Authenticity
Bibliographic record
Abstract
Abstract Traditionally, being authentic is regarded as something one is, in which an entity is judged to possess the appropriate “authenticity” attributes independently acquired outside of marketplace pressures. We consider two factors that challenge this traditional view to arrive at an alternative view of authenticity. First, we draw on existentialism that describes authenticity as a process of self-creation that occurs in interaction with others. Second, we consider neoliberal ideology whereby consumers are regarded as fully immersed market actors. By examining social media influencers (SMIs) who foster authenticity with overt marketization efforts, we identify a marketplace of existential authenticity in which public performances of self-creation by an SMI are accompanied by resolute vulnerability that is valorized as work, thus warranting monetization. This cultural space of existentialism, in which authenticity is something one does (vs. something one is), is governed by a “reflexive synopticon”: a form of governmentality that shapes the actions of participants (SMIs, audience members, and the social media platform) through their mutual reflection on each other. Our work articulates how neoliberal horizontal governmentality structures operate, which includes the enabling (vs. antagonistic) role of the market. Moreover, our findings emphasize the culturally bound nature of different types of authenticities.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.066 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| 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".