‘It was too much technology … I chucked my laptop across the room': young women, networked affect and the positivity imperative
Bibliographic record
Abstract
Engaging with theories of networked affect, this paper highlights the importance of new ways of thinking about young women’s relations with social media beyond ‘good or bad’ narratives, and towards more affective, embodied and agentic relations with digital technologies. Drawing upon interviews with 45 young women (16–25 years), we reveal the emergence of new digital intensities during the COVID-19 pandemic, and the reshaping of young women’s digital encounters. As well as highlighting the importance of digital technologies for escaping the stressors of pandemic life, digital practices surfaced creativity, connection and moments of joy. Yet for many young women, the ‘positivity imperative’ to perform proactive and productive feminine subjectivities during the pandemic re-turned their digital relations, surfacing negative affects (i.e. guilt, anger, shame) about themselves and their bodies. There was a networked affective ‘stickiness’ of these feelings, prompting some to navigate new boundaries around their social media engagement to protect their own wellbeing. In so doing, we see how the pandemic acted as a ‘jolt or spark’, activating new affective entanglements with digital technologies, and evoking new ethical and affective relations of care with bodies and subjectivities.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".