Decolonizing the Presentation of Research Findings: Amplifying Epistemic Authority Through Poetic Re-Storying
Bibliographic record
Abstract
Western-centric epistemologies are often deemed to be more legitimate than non-western ones for driving academic research and knowledge production. As a result, non-western epistemologies are often colonized or silenced during the research process. Decolonizing research practices, such as robust collaboration, mutual respect, mindful listening, and co-constructed interviews offer meaningful opportunities for researchers vested in engaging in research which honors and amplifies a diversity of storied experiences and non-dominant epistemologies. This paper focuses on decolonizing research report writing through poetic re-storying and will include a rationale for and excerpts from a poetic re-storying of research findings from a narrative inquiry project with Parvana, an Afghan woman who until recently was living in Afghanistan; the narrative study is theoretically and conceptually informed by postcolonial feminist theory and the decolonization of research methods. By carefully and collaboratively crafting the research findings in poetic form using original excerpts from open-ended interviews, co-constructed interview conversations, Parvana’s written stories, conversations about artifacts, and other data sources, Parvana and I worked together to amplify and honor her epistemic authority and literacy practices. In addition to presenting the research findings in research participants’ own words, creative re-storying through poetry makes research findings accessible to academic and non-academic audiences alike while also cultivating emotional engagement and empathy.
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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 | 0.088 | 0.157 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.013 | 0.060 |
| Scholarly communication | 0.019 | 0.021 |
| Open science | 0.004 | 0.028 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".