Employability and talent management: challenges for HRD practices
Bibliographic record
Abstract
Purpose The purpose of this conceptual paper is to illuminate the problems that are associated with defining and identifying talent and to discuss the development of talent as a contributor to employability. Design/methodology/approach The world of work is characterised by new and rapidly changing demands. Talent management has recently been the target of increasing interest and is considered to be a method by which organisations can meet the demands that are associated with increased complexity. Previous studies have often focused on the management of talent, but the issue of what exactly should be managed has generally been neglected. In this paper, the authors focus on discussing the substance of talent and the problems associated with identifying talent by using the following closely related concepts: employability, knowledge, and competence. Findings Employability is central to employee performance and organisational success. Individual employability includes general meta‐competence and context‐bound competence that is related to a specific profession and organisation. The concept of employability is wider than that of talent, but the possession of talent is critical to being employable. In this paper, the authors suggest a model in which talent includes individual, institutional, and organisational‐social dimensions. Practical implications The illumination of different meanings of talent management and the substance of talent is crucial to the practical implication of central human resource development practices, such as training and development. Originality/value The paper shows that clarification of the conceptual boundaries and the presentation of a typology that is relevant to the understanding of talent are central to the creation of valid talent management systems that aim to define and develop talent.
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 | 0.069 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.011 | 0.044 |
| Scholarly communication | 0.029 | 0.017 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".