The Influence of Decaying the Representation of Older Social Media Content on Simulated Hiring Decisions
Bibliographic record
Abstract
Decaying representations gradually make social media content less visible to readers over time, which can help users disassociate from past online activities. We explore whether shrinking, one decaying representation, influences managers' assessments and simulated hiring decisions of job candidates, compared to seeing a full profile or an empty profile with no posts. Our 3 x 2 between-subjects crowdsourced survey (N = 360 US managers) shows that shrunk or empty profiles led to more positive decisions than profiles in their original full format. However, shrunk profiles also further contributed to more positive impressions of the candidates. Shrinking did not help the candidate of either gender more than the other and demographics of managers had limited impact on their assessment. Further, our managers regularly search job candidates' social media profiles in real life, suggesting that shrinking could support users' privacy. We finally present implications for individuals' privacy on social media.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".