Walking Alongside: Relational Research Spaces in Visual Narrative Inquiry
Bibliographic record
Abstract
Walking alongside is a phrase used in narrative inquiry to describe relational commitments that shape how we attend to the complexity of lives, unfolding over time, and within a web of social relations. The space of inquiry requires researchers to attend to participants’ lives and stories of experience across various social situations, places, and times. In this paper, I explicate and unpack my intimate, and sometimes complex, journey and unfolding research process. In this study, walking alongside was a process of embodying the relational ethics of narrative inquiry, which attended to silences, remained playful, and responded to and through uncertainty. I provide insight into building relational spaces in visual narrative inquiry by combining art-making with Lugones’ theories on world travelling to creatively and nimbly respond to stories and walk alongside participants. As a narrative inquirer, I walked alongside three trans young adults, to co-create, re-imagine, and transform research in relation to participants. This process is undergirded by attention to and a deepening awareness of relational ethics, and by creating spaces that allow for emergent possibilities of being in relation to honor diverse and multiple ways of knowing.
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 | 0.031 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.051 |
| Scholarly communication | 0.018 | 0.021 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".