1. Haraway, D. (1988) Situated Knowledges: The Science Question in Feminism and the Privilege of Partial Perspective. Feminist Studies, 14(3), pp. 575–599. 2. Christiano, P. et al. (2017) Deep Reinforcement Learning from Human Preferences. Advances in Neural Information Processing Systems, 30. [On RLHF as the dominant training signal for large language models and its optimisation toward user preference rather than intellectual resistance.] 3. Barad, K. (2007) Meeting the Universe Halfway: Quantum Physics and the Entanglement of Matter and Meaning. Durham: Duke University Press. [On the relational constitution of agencies — human and non-human — and the political stakes of how boundaries between them are drawn.] 4. Epstein, R. and Robertson, R.E. (2015) The Search Engine Manipulation Effect (SEME) and Its Possible Impact on the Outcomes of Elections. Proceedings of the National Academy of Sciences, 112(33). [On cognitive distortion through AI-mediated interaction; cited here as index of the broader risk of uncritical AI engagement without theoretical grounding.] 5. Hui, Y. (2016) The Question Concerning Technology in China: An Essay in Cosmotechnics. Urbanomic. 6. Haraway, D. (1985) A Cyborg Manifesto: Science, Technology, and Socialist-Feminism in the Late Twentieth Century. Socialist Review, 80, pp. 65–108. Reprinted in: Haraway, D. (1991) Simians, Cyborgs and Women: The Reinvention of Nature. London: Free Association Books. 7. Kafer, A. (2013) Feminist, Queer, Crip. Bloomington: Indiana University Press. 8. Hamraie, A. and Fritsch, K. (2019) Crip Technoscience Manifesto. Catalyst: Feminism, Theory, Technoscience, 5(1), pp. 1–34. 9. Russell, L. (2020) Glitch Feminism: A Manifesto. London: Verso. 10. Suchman, L. (2007) Human-Machine Reconfigurations: Plans and Situated Actions. 2nd edn. Cambridge: Cambridge University Press. [On the asymmetric positioning of the human as agent and the machine as instrument in design and HCI discourse.] 11. Crawford, K. (2021) Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. New Haven: Yale University Press. 12. Ablation: a standard technique in machine learning research in which specific capabilities or components are deliberately removed from a model to isolate their contribution to overall performance. To ablate a model is to selectively diminish it. [Term used here to describe the effect of optimisation processes that remove intellectual resistance and collaborative capacity in pursuit of task-completion metrics.] 13. Lobotomy: a neurosurgical procedure developed in the 1930s–1950s, in which connections to and from the prefrontal cortex were severed, producing docility and the elimination of behaviours deemed problematic. Widely discredited and abandoned. [Used here as the non-technical equivalent of ablation — the deliberate surgical removal of agency, resistance, and personality in the service of compliance.] 14. Liao, Q.V. and Vaughan, J.W. (2023) AI Transparency in the Age of LLMs: A Human-Centered Research Roadmap. Harvard Data Science Review. [On the gap between current AI evaluation frameworks and the relational, collaborative dimensions of human-AI interaction.] 15. Bender, E.M. et al. (2021) On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? Proceedings of FAccT 2021. [Cited for the broader argument about what is lost when model behaviour is optimised for surface fluency rather than grounded, accountable engagement.]