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Record W6912038988 · doi:10.5281/zenodo.15806290

Survival, Identity, and Power Dynamics: A Comparative Analysis of The Marrow Thieves and Babel

2025· article· en· W6912038988 on OpenAlexaboutno aff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicUtopian, Dystopian, and Speculative Fiction
Canadian institutionsnot available
Fundersnot available
KeywordsColonialismIndigenousPower (physics)Identity (music)SovereigntyNarrative

Abstract

fetched live from OpenAlex

This paper examines how Babel by R.F. Kuang and The Marrow Thieves by Métis author Cherie Dimaline explore the intertwined themes of identity, survival, and power, arguing that both novels use speculative fiction to critique colonial systems that endanger cultural heritage and human dignity. I argue that these themes are central to the characters’ individual experiences and serve as tools for broader social critique within the speculative frameworks of each narrative. Babel critiques how institutional power can elevate and erase marginalised identities, using language as both a weapon and a site of resistance. In contrast, The Marrow Thieves centres identity in Indigenous traditions and ancestral memory, presenting survival as inherently tied to cultural preservation. While Babel situates power within scholarly and magical hierarchies, The Marrow Thieves frames it as a struggle for sovereignty and survival in the face of ongoing colonial violence. By comparing these narratives, this essay demonstrates how speculative fiction can illuminate the persistent effects of colonialism on identity formation and the fight for cultural survival. Keywords: speculative fiction; colonialism; translation; dystopian; power; control; survival; oppression; babel; the marrow thieves

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0200.028
Scholarly communication0.0080.005
Open science0.0010.007
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0070.000

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.

Opus teacher head0.048
GPT teacher head0.269
Teacher spread0.221 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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