Bibliographic record
Abstract
Buy It Now, Michele White examines eBay and its emphasis on community and social norms, revealing the cultural assumptions about gender, race, and sexuality that are reinforced throughout the site. She shows how instructional texts, rule systems, and advertisements "configure the user," allowing eBay to indicate how the site is supposed to function while also upholding particular values and practices. White details how eBay reinforces stereotypes about gender and sexuality, looking, for example, at descriptions included in wedding dress listings, and how eBay directs individuals to the "Adult Only" part of the website when they use the search terms "gay" and "lesbian." She discloses the ways that eBay promises a caring community but its "Black Americana" category reproduces racism by allowing sellers' narratives that excuse and romanticize slavery and insult African Americans. White also looks at how participants challenge eBay's categories, rules, and values, examining widely used strategies of resistance by sellers and buyers in the lesbian and gay interest listings. By analyzing the organizational and cultural logics present in eBay, White emphasizes how other Internet settings, including craigslist, are not as transparent, community-oriented, and empowering as they claim. She proposes methods for researching and reconceptualizing new media sites.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.402 | 0.152 |
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".