THE QUALITY IN QUALITATIVE: AN EXAMINATION OF RETIREMENT INTERVIEWS
Bibliographic record
Abstract
Abstract Qualitative interviews are a dynamic and complex way to understand life experiences. Retirement is an ever-evolving, dynamic, and complex social construct we associate with the end of one’s career. Exploring what retirement means to different people in the context of critical gerontological theories of successful aging can contribute to a better understanding of the implications of this important transition at the individual and societal level. However, sifting through participants stories is not always a straightforward endeavor, particularly in the case when participants have complex and dynamic stories. This paper examines the value of qualitative research methods in unpacking complex personal narratives. As the landscape surrounding mature workers’ experiences continues to change, this paper extends policy debates about retirement, as well as scholarly conversations about the richness and complexity of qualitative research.
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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 | 0.166 | 0.260 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.018 | 0.018 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".