Social Perspectives on Semantic Interoperability: Constraints on Geographical Knowledge from a Data Perspective
Bibliographic record
Abstract
Much attention has been paid by government agencies and GIS researchers to standardization of data and interoperability of systems. Many of these efforts, however, have focused narrowly on technical hurdles while ignoring the social and political contexts that influence interoperability decisions. This article illustrates how social factors influence interoperability along three axes: classification, ontologies of data models, and government policy. Extant research approaches to interoperability of GIS are discussed and their strengths and weaknesses assessed. The article begins with definitions of what interoperability is, why it is important to academic users and policy makers, and its influence on geographical knowledge in a digital age. Exploration of social influences, as an alternative analytical approach to interoperability, begins with a discussion of the roles of classification and scale. The dangers of maintaining inflexible ontologies associated with specific data models are illustrated as a technical limitation with profound social implications for the construction of knowledge. Finally, policy at the multiple levels of governance with respect to interoperability is explored as an infrastructural constraint – and a diminishing influence.
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 | 0.023 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.009 | 0.071 |
| Scholarly communication | 0.024 | 0.034 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".