Ghosts in the Machine: Possessive Selves, Inert Kinship, and the Potential Whiteness of “Genealogical” Indigeneity
Bibliographic record
Abstract
This article explores the recent rise in the use of self-identification as a key element of legitimacy in contemporary claims to Indigeneity. Emphasizing self-identification as a central dynamic of all identity-making in contemporary nation-states, the article argues nonetheless that this element of identity is insufficient for making ethical claims to Indigeneity. Emphasizing instead the importance of ongoing Indigenous relationality (i.e., kinship), it argues that genealogical databases potentially exacerbate the potential to engage in non-relational forms of belonging that undermine Indigenous communities’ and nations’ autonomy in defining the boundaries and contours of their citizenship. I undertake this argument in three broad parts. Part one undertakes a selective discussion of sociologist Stuart Hall’s conceptualization of identity, highlighting what I regard as two relevant elements key to his identity-making framework. Part two then undertakes a brief discussion of Geonpul scholar Aileen Moreton-Robinson’s discussion of white possessiveness as a useful lens for framing the growing self-Indigenization/Pretendianism literature as variegated examples of analyzing its practice; and finally, part three explores the potential of genealogical databases to encourage possessive/non-relational forms of identity-making, what I term here “inert kinship”. The article then concludes with a brief discussion regarding how genealogical databases might be used ethically with respect to claiming Indigenous belonging, and why this is key to the upholding of Indigenous 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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.069 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".