How Organizations Claim Authenticity: The Coproduction of Illusions in Underground Restaurants
Bibliographic record
Abstract
With perceptions of authenticity offering contemporary organizations a key competitive advantage in the marketplace, a growing body of research has investigated “authenticity work”: the diverse ways in which organizational actors fabricate authenticity claims for their audience members. However, claiming authenticity is a challenging and problematic task, because organizations must weigh how much authenticity they can safely project without incurring backfire. This is further complicated by consumers’ fickle and contradictory attitudes regarding authenticity work. This study examines this challenge by asking how organizations can claim authenticity in a way that aligns with their audiences’ variable understandings and expectations. Drawing on a qualitative study of underground restaurants—alternative social dining establishments, also known as “pop-ups” or “supper clubs”—I show that organizers claim authenticity through the coperformance of three illusions: community, transparency, and gift-giving. Instead of rejecting these illusions, most diners and underground organizers knowingly embrace them as authentic. This paper suggests that authenticity work, far from sending a one-way signal that audience members passively accept or reject, involves a continual process that generates the active co-construction of illusions by organizers and their audiences.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.039 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".