Lugones’s Metaphor of “World Travelling” in Narrative Inquiry
Bibliographic record
Abstract
The metaphor of “world traveling” has been taken up by researchers engaged in narrative inquiry. This metaphor provides a reference point throughout the research process for navigating relational processes. In this article, we unpack how the metaphor of “world travelling” shapes the writing of research texts in narrative inquiry and the meaning making inherent in shared texts. We draw on a narrative inquiry focused on the experiences of men who are homeless in Japan, as well as a narrative inquiry focused on precariously housed women who are pregnant or engaged with early parenting and who use illicit substances. As we work with Lugones’s ideas, we see it is critical to engage in a collaborative process that is marked by a playful exchange of ideas and that pushes us to identify connections with others. At the same time, we work to create meaning; we need to challenge racial, social, political, and economic boundaries and social differences in ways that allow researchers and participants to locate themselves in relation to others. Engaging in this manner shows openness to multiple ways of sense making and to creating texts where expectations are broken. Representation requires the development of texts where we can “exercise double vision” and “create and cement relational identities.” These spaces of openness and multiplicity are always in motion and are marked by a sense of “dwelling,” “world travelling,” and “playfulness.”
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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 | 0.017 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.010 | 0.083 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".