Use of Social Networking Sites for Recruiting and Selecting in the Hiring Process
Bibliographic record
Abstract
Business’ departments are already taking advantage of the convenience of technology, while global competition continues to demand creativity in terms of how employers acquire human capital. Thus leading to the question, how can employers take full advantage of technology in the hiring process? For instance, social networking sites (SNS) are being explored as an additional tool for recruiting and selecting the best-suited employees. Even though all types of businesses are taking the initiative to attempt the integration of SNS into their hiring process, many may still lack understanding on how candidate experience influences employer brand image, in addition to its actual benefits and risks. Through the analysis of peer-reviewed journals and other reliable sources concerning the effect of SNS on recruiting and selecting employees, we have found many benefits in the recruiting process, while SNS used in the selecting process could bring further challenges to employers. For instance, lower cost and time per hired employee, ability to reach a high number of possible applicant especially younger generations, ability to attract passive job applicants, and the inclusion of a supplementary method for employee performance predictions are the most important benefits SNS presents to the overall hiring process. On the contrary, legal issues, inability to attract a diverse pool of candidates, the lack of reliability and validity of such platforms, and the overall accuracy of information obtained are risks and pitfalls of the combination of SNS and the hiring process. After examination, we conclude that SNS should be used in recruiting and selecting of employees, but employers should not solely rely on such platforms. Employers greatly benefit from the unbiased information concerning SNS, but as time progresses and processes evolve, further research is always needed in order to reinforce or challenge earlier findings.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".