Talent management: four “buying versus making” talent development approaches
Bibliographic record
Abstract
Purpose This paper presents a typology exploring employers’ perceptions of the quality of available applicants and employers decisions to buy qualified staff vs. to hire available workers and then make i.e. develop them via employer-supported training. Design/methodology/approach This study uses 2015 survey data from Southwestern Ontario, Canada, based on responses from 834 employers regarding their hiring, separations, training and other HRM policies. Findings Among surveyed employers, 10% are “Reliants” who found the quality of available applicants to be low, yet these employers do not provide employee training. Almost half of employers (at 45%) are “Developers” who find the quality of applicants to be low but they do provide employee training. Approximately, 7% of employers are “Poachers” who find that the quality of applicants is high and do not provide employee training, while 38% are Refiners, who find the quality of applicants is high and they provide employee training. Originality/value Employers need to make their training decisions in alignment with their assessment of the quality of job applicants to whom they have access. In this paper, decisions on training and applicant quality are considered concurrently. From an academic viewpoint, the findings raise the issue as to whether other stakeholders (such as educational institutions) are sufficiently helping individuals gain the skills, credentials and work experiences that employers are seeking. If job openings are remaining unfilled because employers are unwilling to hire those available, then applicants lose, employers lose and societies lose.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".