Talent management: four “buying versus making” talent development approaches
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 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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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