Social Perspectives on Semantic Interoperability: Constraints on Geographical Knowledge from a Data Perspective
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it