College recruiting using social media: how to increase applicant reach and reduce recruiting costs
Bibliographic record
Abstract
Purpose – The purpose of this paper is to offer an alternative approach to traditional campus recruiting, using the social media. Specifically, we propose a three-step strategy using Facebook to attract and recruit college graduates. Design/methodology/approach – In Step 1, employers use Facebook to attract as many target students as possible to an employer’s Fan page. In Step 2, employers actively engage with students to enhance their employer brand as a prospective employer. In Step 3, employers initiate a call-to-action to encourage students to act upon a job opportunity and apply for the position. Findings – Social media recruiting can payoff in several ways: First, employers have the advantage of speed through social media recruiting. Second, employers also have broad and frequent access to college students. Employers will also reduce their overall college recruiting costs and lastly, employers enhance their overall employment branding through the use of Facebook for college recruiting. Practical implications – Given the impending retirement of baby boomers, there is an urgent need to recruit college graduates in large numbers. Historically, college recruiting has been the preferred channel; however, few students attend campus career fairs or find information sessions and their campus career centers helpful. As an alternative, employers should consider using social media as a recruiting tool to attract and recruit college graduates. Originality/value – Social media recruiting has the potential to help smaller employers stand out among larger employers, reach out to a larger pool of candidates, speed up the recruitment process and reduce overall recruitment costs.
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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 | 0.015 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.057 | 0.019 |
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".