“It is an Incredibly Painstaking, Time-Taking Domain to Work in”: Examining the Work-Life Tensions and Meaningful Experiences of Data Work in Elite Sport
Bibliographic record
Abstract
This article utilizes empirical insight to critically reflect on the employment and life experiences of data workers in a high-performance environment. The context under study is that of elite sport and the role of performance analyst – a specialist field comprising the use of technology and data in the process of improving sport performance outcomes. Using in-depth semi-structured interviews, the social and organizational environment encompassing data work is explored to examine how it may enable or constrain certain labour practices. The findings reveal implications concerning the nature of data work, and in particular how the pursuit of data at scale escalates issues regarding work-life balance. By acquiring insight into the everyday experiences of analysts and the nature of datafied knowledge production, the study demonstrates how participants find meaning in their labour through establishing credibility and a connection to the affective dimensions of work. We conclude by offering practical recommendations for those entering into this field of work that centre on the importance of enculturation and the collaborative nature of the role, reinforcing the imperative that a human-centred approach to examining data work helps us to better understand how data representations come into being.
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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.033 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".