Decolonizing transformations through ‘right relations’
Bibliographic record
Abstract
Abstract Climate change has been conceptualized as a form and a product of colonization. In this perspective, it becomes important to base climate change adaptation and transformation efforts on decolonizing practices and imaginaries. A central aspect of decolonization is contained in the Indigenous conceptualization of relationality. Exploring how decolonization and relationality might form the foundation for transformations research, we engage with the concept of ‘right relations’. In the context of this inquiry, we take ‘right relations’ to mean an obligation to live up to the responsibilities involved when taking part in a relationship—be it to other humans, other species, the land or the climate. We begin the paper by bringing together the literature on climate change adaptation, transformation and decolonization to show their interconnections and emphasize the need to engage with all three when talking about sustainability. Second, we invoke the idea of ‘right relations’ to address how non-Indigenous transformation researchers can further the process of decolonization as part of their research. Third, we offer insights from our own research experience with narrative practices to help exemplify how transformation researchers in all disciplines might embody ‘right relations’ centered around four characteristics: listening deeply, self-reflexivity, creating space and being in action. Embodying ‘right relations’ is a continuous process of becoming with no end point, and we do not wish to suggest that we hold the answers. Instead, we reflect on our role in this process and hope for these words to open a dialogue about how we might move towards a ‘decolonized humanity’. We suggest that willingness to be affected and altered by the process of reciprocal collaborations is key to imagining decolonial ways of being and that this in turn can be a powerful manner of generating equitable and sustainable transformations.
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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.083 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.006 |
| 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".