Looking Back in Time: The Pitfalls and Potential of Retrospective Methods in Leisure Studies
Bibliographic record
Abstract
An increased focus on alternate theoretical perspectives, methodologies, and methods is needed in leisure studies. Although retrospective methods have been employed in a range of disciplines, criticism has been leveled at their validity, reliability, and trustworthiness. Possibilities and critiques of retrospective methods are discussed as either attempts at controlling or interpreting the past. Techniques for minimizing post-positivist concerns include stimulating memories using cues such as photos, allowing participants to report freely rather than forcing responses, and studying salient phenomenon that are subject to accurate recall. Interpretive methods such as narrative inquiry, autoethnography, and collective memory-work are also discussed and debated.
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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 | 0.548 | 0.622 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.007 | 0.040 |
| Scholarly communication | 0.019 | 0.023 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".