"Actually I Was the Star": Managing Attributions in Conversation
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Bibliographic record
Abstract
In this paper, we outline the parameters of a discursive approach to attributions in sport psychology. Attribution theory has had a strong presence within sport and exercise psychology. Attributions are the perceived causes or reasons that people give for an occurrence related to themselves or others. An attributional model, developed in educational psychology, has been most influential and often requires the researcher(s) or participants to determine the dimensional categorisation of attributions (e.g., internal-external, stable-unstable, controllable-uncontrollable). Assessing attributions in sport and exercise psychology has been almost exclusively through self-report questionnaires and entrenched within a limited theoretical perspective. In contrast, a discursive approach focuses on discourse and what is accomplished through people's talk. Such an approach would advocate a move from a view of talk (discourse) as a route to internal or dimensional categories to an emphasis on talk as the event of interest. Using principles of conversation analysis (CA), a critical examination of the traditional conceptualisation of attributions will be offered in this paper. Drawing on a corpus of data where athletes discuss their sporting performance, we consider the management of attributions as talk-in-action, rather than a series of discrete cognitive elements and dimensions. To illustrate the way that attributions are managed in conversation, we consider three areas—asking questions about loss, the interactional modesty inherent in discussing wins and the "slipperiness" of attributions in conversation. Finally, the implications of a discursive approach to the study of attributions in sport and exercise psychology are discussed. URN: urn:nbn:de:0114-fqs030133
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it