A qualitative approach to understanding the impact of partner play in doubles racquet sports
Bibliographic record
Abstract
The purpose of this study was to examine the impact of partners play on the performance and emotions of doubles racquet sport athletes (badminton, tennis, and squash). Seventeen one-on-one semi structured interviews were conducted over the course of six months' (time of season varied for the different athletes based on sport) with athletes who play doubles racquet sports (i.e., squash, badminton, and tennis). Interviews varied between 32 and 65 minutes in length. Participants were asked how they reacted to different scenarios based on their partners play, and how in turn, their partners play changed (or not) their own performance. Deductive and inductive analyses produced the main themes of negative emotions (i.e., anger), positive emotions (i.e., excitement), and no impact on emotions, as well as overall impact on performance (positive, negative, or no impact) for the different scenarios (i.e., partner playing poorly, compared to partner playing well). These athletes understand that how their partner plays seems to have a considerable effect on not only their emotions, but their own play.
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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".