Bibliographic record
Abstract
This doctoral research examines the Canadian and American grinder scenes to gain insight into the role of senses in understanding and responding to social problems. Grinders, a subset of biohackers, aim to enhance themselves by assimilating emerging material technologies (including, but not limited to, electronics) with their bodies through experiments and surgeries. They opt for a do-it-yourself (DIY) approach in order to maintain a sense of agency that might be lost if pursued through traditional means, such as ‘normalized’ medical research, ethically constrained university research, or market-driven private industry. How do grinders make sense (literally and figuratively) of their bodies as a site for enhancement? \n \nThe research design included three years of virtual ethnography of online grinder hubs, which were connected and contrasted with a concurrent two years of ‘real world’ participant observation ethnography at grinder laboratories and events. Data analysis applied actor-network theory to trace grinders’ sensory assemblages through a variety of on- and off-line sources. These included internet forum posts, IRC chat logs, and blogs, as well as 40 in-depth interviews, dozens of informal interviews, and direct observations of grinders planning, surgically implanting, and using their ‘enhancements.’ Results demonstrated how grinders position their bodies both broadly in relation to their current social circumstances, as well as specifically through three case studies involving magnetic implants, RFID tags, and body-computer interfaces. \n \nThis study is situated in Cyborg Anthropology and Science and Technology Studies to understand the relationship between bodies, technology, and culture. Findings suggest grinders conceive of the human body as an ironic hybrid of positivism and constructionism, determined by its techno-biological material yet simultaneously amenable to endless modification. In practice, however, the results of the tension between stability and variability tend to reinforce hegemonic social and economic relationships. What grinders ultimately enhance is the ability to adapt their physical bodies to social uncertainty brought about by the accelerating digital economy of information.
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 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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.035 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| 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".