The Electronically Activated Recorder (EAR): a novel approach for examining social environments in youth sport
Bibliographic record
Abstract
The interactions between athletes, parents, and coaches outside of the immediate training and competition environments can shape sport participants’ overall experiences. Accordingly, researchers have explored novel approaches that enable the investigation of experiences that occur beyond the sport activity itself. Technological innovations, combined with careful ethical considerations, have led to the development of research methods that can be used to assess participant conversations in their natural sport and social environments. This article introduces sport researchers to the Electronically Activated Recorder (EAR), an ambulatory ecological assessment method that provides access to daily social interactions among athletes, parents, and coaches within and beyond the immediate sport activity (e.g. commute to/from activity, locker rooms, hotels). The EAR software is embedded within a portable device (e.g. Android device) and is programmed to record brief segments of audio from participants’ daily lives. In addition to discussing the utility of this approach for sport contexts, we introduce the Audio Coding System for Social Environments in Sport (ACSSES), which was developed to assess the interactions captured from athletes’ natural sport and social environments using the EAR. Evidence for the reliability and validity of the ACSSES, the associated coder training protocol, and proposed implications for research are discussed.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".