From fashion blogger to media professional: networked blogfriends, proximity privilege and making a media career from the North American fashion blogipelago
Bibliographic record
Abstract
In the two decades since fashion blogging emerged as a distinct sub-genre, a generation of early fashion bloggers has risen to prominence as writers and editors in the media industries, despite not starting their blogs – as many later did – as a route to a fashion-related career. This study traces the career histories of one such networked group of writers. All participated in early fashion blogging and now work in media, mostly in New York and Toronto. Drawing on nine interviews with individuals from the early North American fashion blogipelago, this article argues that their professional success is not simply attributable to personal skill or the visibility of digital communications. Rather, an interplay of several factors gave rise to the opportunities leading to their established media careers: the affordances of blogging to develop professional skills and confidence; participation in networks of ‘blogfriends’, and proximity to one of North America’s media centres. This aspect, dubbed ‘proximity privilege’ by one participant (Mlotek 2019), demonstrates how enduringly geographically embedded the cultural industries are. By inhabiting one of these fashion media centres, even temporarily, the interviewees benefitted from proximity to existing networks and opportunities that consolidated the visibility and skills afforded by fashion blogging.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".