Referral programs as a referral recruiting tool
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
\nThe article analyzes the practice of developing and using referral programs that activate and streamline the use of referral recruiting. The purpose of this study is to analyze the advantages, disadvantages, and features of the content of referral programs as a tool for attracting staff, formulate an algorithm for their development and criteria for evaluating their effectiveness. The study was conducted in the first quarter of 2024 at a large industrial enterprise in Yekaterinburg with more than 5000 employees. According to the study, more than a third of the available vacancies in 2023 at the analyzed enterprise were closed on the recommendations of their employees, as well as former employees. Nevertheless, the potential of the referral program has not been fully realized due to the lack of its integration with other digital recruiting tools and the weak involvement of factory employees in it. The authors have proposed a number of personnel solutions to improve the effectiveness of referral recruiting in the enterprise.\n
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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