Deconstructing the binaries of spatial data production: Towards hybridity
Bibliographic record
Abstract
Abstract Binaries, the most reductive form of categorization, can be usefully invoked to characterize emerging phenomena; yet, they are widely critiqued for oversimplifying a complex world and for their use as tools of social and political influence. Through a literature review and content analysis this article traces the emergence of volunteered geographic information (VGI), and identifies the recurrent use of several related binaries to contrast this phenomenon with the conventional spatial data production activities of states and corporations. Using several key examples, these binaries are deconstructed by identifying a mismatch in how VGI is conceptualized (bottom‐up, amateur, asserted) in the literature and the reality of existing VGI projects. As an alternative to a binary conceptualization of spatial data production, a different representation is put forward that more accurately depicts what is in actuality a vast, shifting, and heterogeneous landscape of spatial data production approaches. Thinking about contemporary spatial data production not as a binary but as a continuum could encourage the development of hybridities that harness the benefits of different approaches—including the oversight and quality control of conventional methods, with the speed, low cost, and distributed nature of citizen‐based spatial data production.
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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 | 0.035 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.005 | 0.056 |
| Scholarly communication | 0.022 | 0.030 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".