A story-net approach to qualitative research: having tea with stories!
Bibliographic record
Abstract
Purpose The purpose of this paper is to share the story-net approach and to situate it as one that benefits from blending story as Indigenous methodology with non-corporeal actant theory (NCAT). The authors hope it will serve useful in building storytelling communities where Indigenous and non-Indigenous scholars are working to heal together from colonial trauma, reveal the inner workings of historical and ongoing colonial projects, dismantle the agency of colonial projects, and welcome heartful dialogue into the centre of MOS discourse. Design/methodology/approach The authors employ a storytelling approach which includes mapping the story-net territory and identifying the plot points along the journey. The authors use the story-net approach to story the approach. Findings This approach served helpful when engaging within story archives and with storytelling collectives comprised of both Indigenous and non-Indigenous persons, peoples and knowledges. The authors found four key premises, which help to narrate the ontology, epistemology, methodology and axiology of the story-net approach and six plot points, which help in mapping the lessons learned from engaging with stories, storytellers, story listeners and the socio-discursive contexts surrounding story-net work. Originality/value The authors story an approach that can be useful to support emerging Indigenous scholars while engaging with their non-Indigenous colleagues to do story-net work. This approach may be useful to navigate the tensions to create safer, more humane, inclusive, relational, strengths-based and trauma-informed spaces for engaging with Indigenous stories, storytellers, story listeners and discourses, as well as, to plot the points of contention so as to set the stage for deepening respectful research relations.
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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 | 0.065 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.014 | 0.034 |
| Scholarly communication | 0.019 | 0.024 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 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".