Bibliographic record
Abstract
Purpose The purpose of this paper is twofold. First, to discuss the application of a multi‐level perspective to signaling theory in a recruitment context. Then to discuss how the integration of signaling theory and the social identity approach may provide an improved understanding of the associations between an organization's recruitment activities and applicant attraction outcomes. The paper, first, summarizes the existing research and theoretical developments pertaining to signaling theory, multi‐level theory, and the social identity approach. From this literature a theoretical model from which research propositions are developed is suggested. Design/methodology/approach This is a literature review, within recruitment contexts, on signaling theory, the association between market signals and applicant attraction outcomes, and the integration of signaling, social identity, and self‐categorization theories as a theoretical foundation for research propositions. Findings Despite widespread acceptance of signaling theory in recruitment research, surprisingly little is known about the boundary conditions in the association between an organization's recruitment activities and applicant attraction outcomes. Practical implications A greater understanding of the application of signaling theory will enable managers to design and administer recruitment activities and processes in order to improve applicant attraction to recruiting organizations. Originality/value This paper fills a void in the recruitment literature by integrating signaling theory, social identity theory, and self‐categorization theory and providing avenues for future work.
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.
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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".