Towards a study of information geographies: (im)mutable augmentations and a mapping of the geographies of information
Bibliographic record
Abstract
Information has always had geography. It is from somewhere; about somewhere; it evolves and is transformed somewhere; it is mediated by networks, infrastructures, and technologies: all of which exist in physical, material places. These geographies of information about places matter because they shape how we are able to find and understand different parts of the world. Places invisible or discounted in representations are invisible in practice to many people. In other words, geographic augmentations are much more than just representations of places: they are part of the place itself; they shape it rather than simply reflect it. This fusing of the spatial and informational augmentations that are immutable means that annotations of place emerge as sites of political contestation: with different groups of people trying to impose different narratives on informational augmentations. This paper therefore explores how information geographies have their own geographic distributions: geographies of access, of participation, and of representation. The paper offers a deliberately broad survey of a range of key platforms that mediate, host, and deliver different types of geographic information. It does so using a combination of existing statistics and bespoke data not previously mapped or analysed. Through this effort, the paper demonstrates that in addition to the geographies of uneven access to contemporary modes of communication, uneven geographies of participation and representation are also evident and in some cases are being amplified rather than alleviated. In other words, the paper comprehensively shows one important facet of contemporary information geographies: that geographic information itself is characterised by a host of uneven geographies. The paper concludes that there are few signs that global informational peripheries are achieving comparable levels of participation or representation with traditional information cores, despite the hopes that the fast‐paced spread of the internet to three billion people might change this pattern.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.004 | 0.048 |
| Scholarly communication | 0.015 | 0.033 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".