Green recruitment and selection: an insight into green patterns
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
Purpose Although the role of green recruitment and selection (GRS) has been widely recognised as an important dimension of green human resource management, no study has ever mapped the terrain of GRS and reviewed the literature. The purpose of this paper is to fill this gap while exploring the following questions: How do organisations select candidates in line with their pro-environmental stance? What impact do a company’s corporate environmental sustainability (CES) practices have on attracting pro-environmental job seekers? Design/methodology/approach This paper provides a systematic review of 22 peer-reviewed articles published during the period 2008–2017. The articles were included in the review if they addressed at least one of the two research questions. Findings Some companies choose to apply green criteria when selecting candidates while others do not. In any case, communicating a company’s environmental values and orientation is worth practicing during GRS. Previous studies have identified four mediators (anticipated pride, perceived value fit, expectation of favourable treatment, perceived organisational green reputation/prestige) that intervene between signals of a company’s CES and a job seeker’s perceptions of organisational attractiveness. However, the strength of this effect is influenced by five moderators (pro-environmental attitude, socio-environmental consciousness, desire to have a significant impact through one’s work, environmental-related standard registration, job seeker’s expertise). Originality/value This paper provides the first systematic review of GRS and thus paves the way for future research.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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