Metal and Flesh, and: Cyborg: Digital Destiny and Human Possibility in the Age of the Wearable Computer (review)
Bibliographic record
Abstract
We take our tools for granted. Even those that we carry on our bodies, such as eyeglasses or palmtops, we consider as add-ons, foreign objects. Although contact lenses or pacemakers acquire a certain degree of intimacy, even they are still perceived as mere "add-ons," not part of our organic flesh or mind. Should we be made aware of the hidden effects of technologies both on body and mind, or should we continue in the blissful ignorance of our own transformation? Two books by Canadian authors explore that question in complementary ways. Ollivier Dyens' Metal and Flesh (translated from the French in this edition by MIT Press) practices "depth philosophy"—as in "depth psychology"—to find out what makes us human with or in spite of technology. Steve Mann does the experimental grunt work: his Cyborg is a detailed analysis of the tools themselves and of their present and predictable consequences. Both writers take McLuhan seriously and quote his lesser known paraphrase of "The medium is the message": "We shape our tools and hence after, our tools shape us."
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".