Navigating person-branding in the fashion blogosphere
Bibliographic record
Abstract
Fashion blogs have received much attention since their emergence in 2002. Yet, little is known about how fashion bloggers succeed or fail in building their brand in the fashion industry. This article examines how fashion bloggers navigate person-brand building by focusing on how fashion bloggers accumulate – or fail to accumulate – status and audience, on the basis of a new form of capital and construct: person-brand capital. Based on an 18-month netnography in the fashion blogosphere and a Bourdieuian theoretical approach, we find that to build a strong person-brand, fashion bloggers must engage in at least two sets of practices that help fuel person-brand capital. Fashion bloggers must signal that they belong to and play a valuable role in the field of fashion and in the subfield of blogging. Our findings also demonstrate that engaging in practices rooted in either a lack of cultural capital in the field of fashion or weak social capital in the subfield of blogging can hinder person-brand capital development. Overall, our research provides insight into successful person-brand building in the fashion blogosphere and offers implications for fashion brands that want to benefit from the unique showcase that they can offer.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".