Scaling and controlling talent development in high-intensity organizations: the case of a Swedish football club
Bibliographic record
Abstract
Purpose In this paper, the authors empirically and theoretically analyze the scaling and control of talent development to highlight an important part of commercialization in football clubs, especially in the light of a growing transfer market. Design/methodology/approach Conducting a single case study of a Swedish football club, the authors adapt a view of the club as a “high-intensity” organization (Alvesson and Kärreman, 2004), one that inherently relies on strong identification of employees and the fostering of talent. This view allows us to detail the importance of both socio-ideological and technocratic forms of control involved in the talent development process. Findings The authors show how socio-ideological and technocratic forms of control were combined to establish the football club as a “talent factory” in the league, as well as the corresponding challenges when scaling talent development activities and how these challenges were handled. In doing so, the authors contribute to the broader accounting literature on talent- and human resource management, as the authors provide an example of how football clubs may commercialize without necessarily violating their fundamental sports values. Originality/value Talent management has mainly been studied in terms of increasing player wages and a focus on the cost of talent. As opposed to these perspectives, the authors highlight the revenue potential in developing players in the light of a growing transfer market and the relevance of talent development for the commercialization of football clubs.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.004 |
| 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".