"The Power of Personal Experiences": Post-Publication Experiences of Researchers Using Autobiographical Data
Bibliographic record
Abstract
Although much has been written about the challenging writing process associated with autobiographical research, little is known about the post-publications consequences of using personal experience as a primary source of data. This psychology honour’s project used an online survey to investigate the question: What are researchers’ experiences and perspectives after publishing research that used autobiographical materials as the primary source of data? The participants were 13 individuals who had published at least two autobiographical peer-reviewed articles and the method was qualitative description using content analysis. Primarily positive findings were identified (e.g., career advancement, professional and personal validation, perceived strengthened relationships with others) although some participants continued to wonder about decisions related to their autobiographical publications (e.g., privacy of third parties, what content to include or exclude) and about the reactions of others (e.g., readers, loved ones). Findings underscore how using personal experience as data blurs the borders of scholarship and personal growth, and directly impacts audiences. Implications include tips for those interesting in doing autobiographical 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 | 0.064 | 0.161 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.016 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.005 |
| 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".