Researcher Subjectivities as a Conceptual Frame in Collaborative Research: How Exploring the Experiences of Adult Educators Led to Examining Researcher Lenses
Bibliographic record
Abstract
This article discusses how narrative research that began as an exploration of adult educator practice transitioned to incorporate an examination of researcher lenses, ultimately resulting in an increased understanding of the critical role that researcher subjectivity plays in the collaborative qualitative research process.First, we discuss the aims and methodology of our original research study, which explored significant experiences in adult educators' practice. We examine how our focus shifted during the analytical stages of this research to explore our own presence in the research. We then discuss each of our own subjectivitiesand how our individual researcher lenses influenced our collaborative research. Next, we detail our research findings, exploring how acknowledging our own subjectivities altered our approach to the data, helping us to reconceptualize the 14 initial themes in our interviews with adult educators to three overridingones: meaningfulness and ambiguities, power and critique, and reflection and authenticity. We then discuss emerging issues about collaborative inquiry and how our subjectivities as researchers construct lenses that continually inform our research processes, including the analysis and interpretation of data. We concludethat researcher subjectivities, when overtly invited into the research process, can become powerful tools in collaborative qualitative research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.240 | 0.187 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.033 | 0.142 |
| Scholarly communication | 0.035 | 0.037 |
| Open science | 0.005 | 0.036 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".