Platformed cultural production and calibration in the Covid-19 pandemic
Bibliographic record
Abstract
The COVID-19 pandemic created a period of social and economic crisis that introduced two distinct problems for social media influencers. At the same time that the pandemic made their work economically precarious, it also made their work morally hazardous, as large-scale human suffering made influencers’ lifestyle promotions appear out of step with their audiences’ day-to-day experiences. How did influencers and the personnel they work with organize their labour to navigate uncertainty and avoid moral criticism? Drawing on 40 in-depth interviews with fashion influencers as well as the industry personnel they work with, I explain how influencers and those close to them respond to and combat issues of uncertainty and change during a period of crisis. I pair this interview data with a year-long online observation of influencers’ labour online. In a calibrated move from aspiration to authenticity, influencers stressed the ‘ordinary’ and ‘everyday’ qualities of their lives during the pandemic, evading moral sanctions against profit-making. Throughout, they leveraged their tentacular connections with audiences to refine content in step with shifting demand and desire online, maximizing their market reach and annual revenue.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.028 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.011 |
| 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".