Battling the war for talent: an application in a military context
Bibliographic record
Abstract
Purpose The purpose of this paper is to introduce a comprehensive new recruitment model that brings together research findings in the different areas of recruitment. This model may serve as a general framework for further recruitment research, and is intended to support Human Resource managers in developing their recruitment policy. To highlight its utility, how the model can be applied to describe the recruitment process of the military is exemplified. Design/methodology/approach The model is developed based on an extensive search for published studies on employee recruitment and on the efforts of the members of the NATO Task Group on Recruitment and Retention of Military Personnel. Findings The model proposes that individuals' cognitions (beliefs, perceptions, expectations) influence job pursuit behavior, via influencing job pursuit attitudes and intentions. Individuals' cognitions are shaped by information about job and organizational characteristics. Job/organizational information can be obtained from sources that are or are not under the direct control of the organization. Finally, several inter‐individual difference variables (e.g. values, needs) are proposed to moderate the relationships depicted in the model. Originality/value The model extends previous recruitment models through its integrated focus on both the applicant's and organization's perspective, its recognition of the multiphased nature of recruitment, and its applicability to real‐life recruitment contexts.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".