Kincentricity: Rethinking Data Literacy
Bibliographic record
Abstract
Abstract Over generations, Indigenous Peoples have developed their own systems of knowledge. The translation of this knowledge into data is evident in various forms of recordings, such as oral narratives, songs, and other artifacts that preserve and share essential information for the community (Carroll et al., 2019). However, colonial assimilation policies have sought to erase Indigenous knowledge and rewrite their histories in favor of the colonial project. In response, Indigenous communities are promoting data sovereignty initiatives and engaging in ongoing efforts to develop IS solutions that integrate their cultures and worldviews. These initiatives reflect a commitment to preserving their identities while adapting to the digital context. Data literacy, which is the ability to create, collect, analyze, and use data, offers a powerful opportunity for Indigenous Peoples to strengthen their autonomy and use their data in a representative way. Although every Indigenous community has its own unique characteristics and culture, a common element shared among most Indigenous cultures is the importance of relationships with people, animals, and nature (Wilson, 2008). This centrality of relationships reflects an Indigenous worldview called kincentricity, which sees all beings, living and non-living, as interconnected relatives (Salmón, 2000). In an Indigenous kincentric view, data emerges from relationships and belongs to a network of responsibilities involving people, the earth, and all beings. This understanding is in line with the perspective articulated by the First Nations Information Governance Centre (2023), which emphasizes that “our data come from us, they connect us, they define who we are and have the power to guide the construction of our future in our own image” (1:39). Similarly, Carroll (2024) affirms that “we claim data back as our relatives” (2:58). In these affirmations, data are not merely instruments of representation or measurement, but expressions of relational responsibility and community continuity. From a relational point of view, the way data are conceived and used reflects profound epistemological differences from dominant Western paradigms. While the Western approach to data emphasizes objectivity and tends to fragment data and treat it as isolated units of information, the Indigenous perspective on data is deeply rooted in relationality, recognizing its interconnectedness with biological, philosophical, spiritual, and actionable aspects. In this TREO, I argue that kincentricity is a relevant analytical lens for investigating how Indigenous Peoples understand data. By situating data in networks of relationships, this lens contributes to a deeper understanding of data literacy in Indigenous contexts and to the recognition of its importance to data sovereignty.
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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 | 0.047 | 0.118 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.009 | 0.033 |
| Scholarly communication | 0.013 | 0.027 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".