Ghosts in the Machine: Possessive Selves, Inert Kinship, and the Potential Whiteness of “Genealogical” Indigeneity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it