Bibliographic record
Abstract
We relate to things and things relate to us. Emerging technologies do this in ways that are interesting and exciting, but often also inaccessible or invisible. In this open access book, leading design researchers and philosophers respond to issues raised by this situation — inquiring into what it means to live with and relate to things that can actively relate to us, and that relate to each other in ways that do not involve us at all. Case studies include Amazon's Alexa, the Internet of Things, Pokémon Go and Roomba the robot vacuum cleaner. Authors explore everything from the care work undertaken by objects, reciprocal human/machine learning, technological mediation as a form of control, and what it takes to reveal things that tend to be hidden and that often (by design) conceal the ways in which they use us. As a whole, Relating to Things is a collaborative philosophical inquiry into the nature and consequences of contemporary technological things. It is a design inquiry into the current nature of the artificial, and possibilities for how things might be otherwise. The eBook editions of this book are available open access under a CC BY-NC-ND 4.0 licence on bloomsburycollections.com.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.069 | 0.034 |
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".