How Do You Study Recruitment? A Consideration of the Issues and Complexity of Designing and Conducting Recruitment Research
Bibliographic record
Abstract
Research on recruitment has increased in both quantity and quality over the last decade, and several review articles have appeared during this time. Previous reviews, however, focused on recruitment topics, models, and theory. This chapter focuses on the design and methodology of recruitment research. It introduces a model called the Recruitment Research Design Model (RRDM) that describes how recruitment research can be designed and conducted within and across four stages of the recruitment process: application, interaction, job offer, and socialization stages. Using the RRDM, it then shows that there are four different ways to design recruitment research: single-stage designs, cross-stage designs, multiple-stage designs, and sequential-stage designs. After describing each design, this chapter provides examples from the recruitment literature of studies that have used each of the four designs. It then describes the following five methodological issues that are important considerations for designing recruitment research: levels of analysis, research methods, study participants, recruitment practices, and recruitment outcomes. Guidelines and decision points for designing recruitment research based on the RRDM and the five methodological issues are then described.
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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 | 0.416 | 0.510 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.010 | 0.024 |
| Scholarly communication | 0.023 | 0.033 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.015 | 0.021 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".