Bibliographic record
Abstract
An analysis of the ways that software creates new spatialities in everyday life, from supermarket checkout lines to airline flight paths. After little more than half a century since its initial development, computer code is extensively and intimately woven into the fabric of our everyday lives. From the digital alarm clock that wakes us to the air traffic control system that guides our plane in for a landing, software is shaping our world: it creates new ways of undertaking tasks, speeds up and automates existing practices, transforms social and economic relations, and offers new forms of cultural activity, personal empowerment, and modes of play. In Code/Space, Rob Kitchin and Martin Dodge examine software from a spatial perspective, analyzing the dyadic relationship of software and space. The production of space, they argue, is increasingly dependent on code, and code is written to produce space. Examples of code/space include airport check-in areas, networked offices, and cafés that are transformed into workspaces by laptops and wireless access. Kitchin and Dodge argue that software, through its ability to do work in the world, transduces space. Then Kitchin and Dodge develop a set of conceptual tools for identifying and understanding the interrelationship of software, space, and everyday life, and illustrate their arguments with rich empirical material. And, finally, they issue a manifesto, calling for critical scholarship into the production and workings of code rather than simply the technologies it enables—a new kind of social science focused on explaining the social, economic, and spatial contours of software.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.050 | 0.014 |
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".