Goal Setting in Masters Sport An Autoethnography of a Masters Kettlebell Athlete and Coach
Bibliographic record
Abstract
The purpose of this paper is to describe the nuances of goal setting in coached Masters sport from the perspective of a Masters athlete (MA) who is also a Masters coach. Thus, this paper is an autoethnography of how the first author’s experience in goal setting plays out as a MA with an online coach, and as a coach, coaching other MAs in-person. Data were collected through a series of guided reflexive journal entries, prompted by follow up questions from the second author to create narrative visibility. Journal entries were analyzed to determine what, when, where, how, and why the first author uses goal setting. In Masters sport, goals are set using many of the same principles described in the literature from different sport contexts; however, this autoethnography indicates that it is important for goal setting to originate from the MA to account for individual motives, and then to be communicated with, negotiated by, and supported from the coach with an interdependent, adult-oriented approach.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".