Geo-cybernetics: A New Avenue of Research in Geomatics?
Bibliographic record
Abstract
The term “geomatics” has existed in the literature for more than a decade, but an overall consensus on the definition of this emerging discipline has not yet been found. The knowledge domain of geomatics has developed in the “interaction space” among converging disciplines (e.g., geographic information systems, cartography, remote sensing, geodesy, and photogrammetry), but its borders are “complex and fuzzy.” Taking cybernetics, both classic and second order, general systems theory, modelling, and complexity as basic building blocks, the research group at CentroGeo is conducting empirical and theoretical work on three main avenues of research: (1) cybercartography, (2) complex solutions in geomatics, and (3) collective mental maps. Recent research results on cybercartography have indicated the value of building a comprehensive theoretical framework that would combine the essence of these three research avenues as a body of knowledge and add to the base of knowledge on geomatics. This article discusses the cybernetic nature of these three research avenues from a theoretical perspective and points to possible areas for further research. In so doing, the authors illustrate the benefits of taking a fresh look at the linkages between cybernetics and geomatics and identify the main elements required to develop a theoretical framework for the concept of geo-cybernetics.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.003 | 0.034 |
| Scholarly communication | 0.016 | 0.033 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 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".