Talent Management Programmes at British, American and Canadian Universities: Comparative Study
Bibliographic record
Abstract
Abstract The article deals with the peculiarities of talent management programmes implementation at the top British, American and Canadian universities. The essence of the main concepts of research - talent and talent management - has been revealed. Talent management is referred to as the systematic attraction, identification, development, engagement, retention and deployment of those individuals who are of particular value to an organization, either in view of their “high potential” for the future or because they are fulfilling business/ operation-critical roles. The factors that drive the development of talent management at the universities have been defined. The benefits that can be obtained as a result of talent management programmes implementation in higher education institutions have been pointed out. The differences in talent management programmes implementation at the universities of Great Britain, the USA and Canada have been found out. These differences depend mainly on the human resources policy of the institution represented in its strategic plan. It has been concluded that most top British and American higher education institutions run talent development programmes, but the target categories and forms of their implementation greatly differ. Canadian universities in the human resources policy focus on professional development of staff and faculty, but do not have special talent management programmes. Progressive conceptual ideas of foreign experience that can be used in practice of Ukrainian universities have been considered.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.019 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".