Deep cuts and painful scars: Athletes' experiences of deselection in youth sport
Bibliographic record
Abstract
Deselection, or being cut, is an inevitable aspect of competitive youth sport. Previous research has examined the social consequences of being cut, loss of athletic identity following deselection, as well as how coaches communicate nonselection to youth athletes. However, little is known about the actual experience of being cut. The purpose of this phenomenological inquiry was to gain insight into what it is like for competitive youth athletes to be cut from a sports team. Lived experience descriptions were collected through semi-structured interviews, written accounts, and informal conversations with 7 female participants (M age = 25.6 years). All participants competed in competitive youth sport at the regional or provincial level and were cut from sport between the ages of 13 and 18 years. Analysis was guided by hermeneutic reflection involving eidetic reduction to reveal the invariant aspects of the phenomena. The experience of being cut from a team may be felt keenly, viscerally even, and seems to resonate with young athletes at a physical and emotional level. The initial shock of a deep cut, being cut down to size, an open cut, masking the pain, tears of pain, and a painful scar are themes uncovered in the participants' anecdotes. Although athletes were cut from sport in different ways, the essence of the athletes' experiences are remarkably similar and sharply recognizable. These findings reveal the lived experience of being cut from youth sport and may provide some considerations for coaches and parents of youth athletes.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".