Shades of digital deception: Self-presentation among men seeking men on locative dating apps
Bibliographic record
Abstract
In recent years, location-based real-time dating apps like Grindr and Tinder have assumed an increasingly pivotal role in brokering socio-sexual relations between men seeking men and have proven to be fertile ground for the study of identity negotiation and impression management. However, current research has given insufficient consideration to how various contextual elements of technology use interact with one another to shape self-presentation behaviour. Through analysis of interview data, we found impression construction on these apps reflects tensions between authentic depiction of the self-concept and self-enhancement via deception. Whether and the extent to which one engages in deception depends on how a number of technological affordances, platform-specific community norms and userbase characteristics interact with each other. Self-presentational choices were a result of a combination of deception facilitators, for example, belief in the normalcy of lying, and constraining determinants, for example, the expectation of brokering physical connection. Impression construction determinants also interact in ways where the influence of any one element is dependent on others. This was most plainly evidenced in the interactions between stigma management concerns, the affordances of audience visibility/control and locatability and common ground reinforcing social hierarchy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".