Organizational attractiveness: Targeting prospective employers on social networking sites
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
BACKGROUND: Many business organizations have integrated the use of professional social networking sites into their HR practices in order to communicate with and attract qualified candidates as part of their talent acquisition strategy. OBJECTIVE: The aim of this research is to explore some social and behavioral signals on social networking sites that enhance organizational attractiveness. Grounded in the signaling theory, this paper fills the research gap by investigating new types of signals on public professional social networking sites that can affect organizational attractiveness as an employer. METHODS: In this research, a quantitative research methodology was used. The sample consists of 288 job applicants using social networking sites in Canada. RESULTS: The results highlighted the importance of social and behavioral factors that play a significant role in enhancing organizational attractiveness on professional social networking sites. CONCLUSIONS: The results provide insights and practical suggestions for managers who decide to integrate social networking sites into their practices. Additionally, the findings of this research help the managers to better understand the factors that have an impact on job applicants’ choices of their future job and employer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 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 it