Rethinking human–technology relations: exploring the sociopolitical dimensions of invasive brain stimulation
Bibliographic record
Abstract
Approaching the meeting of human and technology as a relation involves recognition of the plethora of already existing connections and associations between humans and non-human artifacts, as well as how connections grow and change over time. When connection between human and technology is a relation, it has no clear beginning or end point, no before or after. Instead, there are variations and modifications in the strength and quality of the relation. This paper argues that adequate conceptualization of human-technology contact as an ongoing relationship rather than a discrete interaction requires a broadening of scope beyond individual human users and technological devices. Using the application of deep brain stimulation (DBS) for cases of mental illness as an example, the paper explores how a relational approach to analysis brings forward important ethical and social issues warranting further scrutiny. Historical narratives of device development interpreted through theoretical perspectives of critical disability studies facilitate consideration of how social preferences for “normality” shape user perceptions of safety and effectiveness of yet clinically unproven treatments. Exploring experiences of embodiment through critical disabilities and posthuman critiques foregrounds how vulnerability and resilience are constructed through relational networks of interdependence, themselves novel forms of care. Finally, application of interdisciplinary perspectives to the study of human-technology relations opens up new analytic approaches for studying questions of embodiment, agency, and control precipitated through the development, surgical insertion, and experimental use of technology. Expanding beyond human-technology interactions to analyze the complexity of ongoing relations facilitates an opening-up of discussions around implanted technologies like DBS and invites further investigation of how novel human-technology pairings have the potential to effect social and political change.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".