Decolonizing methodology: Co-designing research with the voices and footprints of the community
Bibliographic record
Abstract
This article responds to calls for a relational praxis and rigor in qualitative research, seeking alternative ways of knowledge co-construction that serve the needs of marginalized and racialized communities, thus subverting research-as-usual designs. I describe the collaborative development of a research methodology within a partnership involving members of the Callemar township's educational community in the Colombian Caribbean region. Conducted over 18 months, this research aimed to co-design decolonizing language curricula with rural teachers and their educational community. Data for this article stems from the first 5 months of the partnership, utilizing a variety of data collection methods, including field notes, audio and video recordings from both online and in-person meetings, and participant-generated media such as videos, audio recordings, and photos during community walks. The primary focus of this phase was analyzing participants’ interactions and historias that shed light on the values that informed the ways of knowing and being in the community. Guided by decolonial, participatory, and community-based methodologies, the central inquiry of this research was: How does a rural community's lived relationality frame the co-construction of a research methodology within a partnership with schoolteachers and an allied researcher? This question was addressed by demonstrating how community dialogue initiated a co-theorizing process that facilitated joint knowledge-making, leading to a deeper understanding of the community's local literacies. These findings contribute to ongoing decolonizing research efforts that aim to center relationality and to challenge the coloniality embedded in traditional knowledge-making practices. This article models a decolonial approach, emphasizing the importance of community collaboration in research. By centering the voices of those directly impacted by educational practices, this research offers valuable insights for future initiatives, promoting a forward-thinking, applied practice in collaboration with communities.
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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 | 0.273 | 0.185 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.012 | 0.053 |
| Scholarly communication | 0.017 | 0.023 |
| Open science | 0.005 | 0.022 |
| Research integrity | 0.005 | 0.009 |
| 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; 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".