Employers’ Use of Young People’s Social Media: Extending Stakeholder Theory to Social Media Data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In a neoliberal economy, businesses are increasingly engaging in social media screening, also known as cyber-vetting, as part of their hiring process. Using an online survey with 482 participants, our research investigates young people’s concerns with their publicly available social media data being used in the context of job hiring. Grounded in stakeholder theory, we analyze the relationship between young people’s concerns with social media screening and their gender, job seeking status, privacy concerns, and social media use. Overall, young job applicants, as stakeholders, have mixed levels of comfort with social media job screening, yet the majority express some concern. We extend stakeholder theory to identify how social media data ethics should be inextricably linked to business practices. The research is contextually situated at the interplay of the systemic constraints of profit-maximization and the necessity of hiring the best people with limited resources, the organizational response of engaging in social media job screening, and the individual response of people expressing privacy concerns. The findings have theoretical implications for a more nuanced conceptualization of stakeholders in an age of social media and practical implications for organizations engaging in cyber-vetting.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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 it