Competition for playing time within elite sport teams: A conceptual model
Bibliographic record
Abstract
Competition is integral to sport and it occurs frequently in various contexts. For example, it takes place during the pursuit of pleasure (e.g., recreational sport) or in the pursuit of excellence (e.g., elite athletics) (Chelladurai, 2012). In the latter context, competition occurs between teams (i.e., inter-team) but also within teams (i.e., intra-team). Harenberg et al. (2010, 2013) reported that competition for playing time (i.e., positional competition) is a central process in intra-team competition and defined it as teammates vying for the same limited playing time with the coach’s awareness. The objective of this research was to gain an understanding of positional competition in an elite sport context. To this end, current literature along with data from two pilot studies was used. This work resulted in a preliminary model which includes the (a) inputs, (b) processes, and (c) outcomes of positional competition. Inputs that impact the nature of the processes include: the characteristics of the individual athlete (e.g., ability, competitiveness) and the team (e.g., norms), along with coaching decisions (e.g., performance roles). The processes include information (e.g., self-awareness) and developmental (e.g., perceived progression, effort) aspects. The processes result in individual (e.g., performance, status) and team outcomes (e.g., collective performance, cohesion). The proposed model adds to our current understanding of the competition between teammates of elite sport teams and the circumstances by which it might result in positive, rather than negative outcomes (i.e., cohesion, group and individual performance). Implications for the practitioner and researcher will be discussed.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".