Bibliographic record
Abstract
Abstract Personal but Not Private is about how people put themselves out there on social media despite the challenges of negotiating overlapping and unknown audiences within platforms’ technological, social, economic, and political contexts. Focusing on queer women’s use of mobile apps, the book develops the concept of identity modulation: the processes through which people and platforms modulate—adjust or modify—personal and intimate information. Through qualitative digital and traditional research methods, identity modulation is investigated in queer women’s use of Tinder, a dating and hook-up app; Instagram, a photo- and video-sharing app; and Vine, an app that enjoyed a brief stint of popularity for its short, looping videos. Across these apps, identity modulation involves users and platforms shaping particular dynamics of personal identifiability, reach, and salience in relation to self-representations. Tinder intensifies personal identifiability by importing profile information from other platforms. Instagram and Vine are configured to extend users’ reach through attention-grabbing media formats while Vine, in particular, facilitated salient expressions of sexuality and identity statements. Queer women responded to these affordances, making the apps work for their aims of forming relationships, increasing their social and economic participation, and countering intersecting forms of oppression. However, platform designs, business models, and governance approaches placed limitations on these women’s agency in their identity modulation. These findings point to the need for sociotechnical changes that give individuals greater control over identity modulation as a means to fully realize its world-making potential.
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 | 0.002 | 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 | 0.013 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.039 | 0.008 |
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".