Research Imitates Life: Researching Within Your Lived Experience
Bibliographic record
Abstract
This personal narrative article seeks to bring awareness to and provide an overview of the various aspects that come with being a lived experience researcher including the host of benefits and challenges that come with conducting research within one's own area of lived experience. Throughout this narrative, we (Bethany Donaghy, an autistic person, and Delane Linkiewich a person living with chronic pain) share our perspectives on what it is like to be lived experience researchers. Our narratives discuss how the many identities we hold both improve the impact and relevance of our research while also posing challenges for us like the additional responsibilities we hold and the reflections we have to make. Most importantly, we present several recommendations to all researchers on how to promote inclusive spaces and increase respect and appreciation for the expertise that people with lived experience hold. This piece outlines critical considerations of what may be considered as best practice for future inclusive research and we encourage researchers to actively consider embedding these recommendations within their own working practice.
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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 | 0.029 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.019 | 0.060 |
| Scholarly communication | 0.022 | 0.025 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".