Bibliographic record
Abstract
Abstract Sneakerheads are individuals who collect and wear sneakers with great enthusiasm. Most of the sneakers that they covet are limited in quantity and worn by celebrities. Sneakerheads’ culture has not been scrutinized in academia, even though it is characterized by some unique behaviours (e.g., purchasing numerous pairs of sneakers, camping out to purchase newly released sneakers and violent incidents). This exploratory netnography research focuses on an online brand community of sneakerheads, Niketalk.com, and explores its members’ information-sharing behaviours and how these behaviours influence their purchase decision-making processes. Data from two Niketalk.com threads about retro sneaker were analysed. Three thematic categories pertinent to sneakerhead culture emerged from the qualitative data analysis. First, ‘release information’ delineates the key information that sneakerheads share online. Second, the heavy usage of ‘jargon and abbreviations’ reveals how sneakerheads interact with each other. Finally, ‘resemblance, rarity, and inequity’ explains what drives sneakerheads’ purchase decision-making, loyalty to their culture and withdrawal from it. The research suggests that sneakerheads need to be aware of the factors that can drive unnecessary impulse purchases, while sneaker brands need to diversify release channels and monitor brand communities to determine the optimal release amounts that can best benefit them. Furthermore, the brands are expected to control the spread of leaked and false information that can negatively impact anticipated product releases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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 teacher head, 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".