Data and Technological Spatial Politics
Bibliographic record
Abstract
The chapter traces the contested politics of data, shifting scales from the transnational to the national and local levels: from questions of the (extra-)territoriality of data, the role of jurisdictions and contested ‘technical territories’ to the concrete lived spaces where data are produced, stored, and circulated. The different contributions thus zoom in from global geopolitical struggles over digital sovereignty and hegemony over data infrastructure to local contestations over subsea cable networks and landing stations, data centres, as well as neighbourhood gentrification driven by AI-development. This multiscalar approach to data politics aims to emphasize the tensions between the abstract global logics of data circulation and the local realities of data, between historical state and corporate projects of extending data territories as a form of ‘domination’, and the localized effects of such projects, including gentrification, expropriation, and the colonial erasure of local knowledges and sovereignty. At the micro level, several of the contributors to this volume explore community activism through a case study of Montreal, where local activists oppose processes of gentrification and displacement driven by an emerging AI ecosystem meant to boost Canada’s innovation and platform economies. We home in on instances of community mapping that produce data in a fair and equitable way; data that empower communities to resist gentrification and expropriation and to support situated knowledges.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.026 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".