Kampen om inflytande vid rekryteringar i svensk elitfotboll : En kvalitativ studie om dataanalysens roll och kommunikativa legitimering i spelarrekrytering i svensk elitfotboll
Bibliographic record
Abstract
This qualitative interview study examines the Swedish elite men’s football clubs AIK, IK Sirius FK, Västerås SK and Örebro SK with the aim of analyzing which actors hold the most power in player recruitment, and how data analysis is communicated throughout this process. The central findings highlight how the structure of the recruitment process and the legitimization of decisions are shaped through an interplay between human and non-human actors. Communication between these actors constitutes both the recruitment process itself and the use of data analysis within it. The study is based on semi-structured interviews, participant observation, thematic analysis of empirical material, and a theoretical framework combining Actor-Network Theory and the Montreal School’s Communication as Constitutive of Organization-perspective. This is complemented by previous research in the field. The theoretical foundation treats human and non-human actors as equally significant, and views reality as socially constructed through communicative processes. The player recruitment process is built upon an interaction between what this study refers to as subjective and objective scouting. Subjective scouting involves human observation through video or live match attendance, where gut feeling, intuition, and the ”football eye” are decisive. Objective scouting, on the other hand, centers on data analysis based on club-selected KPIs. To legitimize decisions, stability within the decision-making network is required, with core actors being club identity, financial resources, the sporting director, and a governing document referred to in the study as the ”player profiles”. These profiles constitute the most stabilizing actors in the network. Data analysis is communicated through several actors. The sporting director, scouts, and analysts all give ”voice” to data during decision-making. Moreover, through documents like the player profiles, data analysis gains significant material influence, as these profiles serve as reference points in many communicative exchanges. The profiles themselves are based on KPIs shaped by data, club identity, financial conditions, and the subjective assessments of human actors. Although data analysis is often presented as objective, in practice it is shaped by subjective choices, interpretations, and communicative translations. The study problematizes the notion of objectivity and demonstrates how it is constructed through interactions between various actors.
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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 | 0.037 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".