WHAT ISWEBMAPPING ANYWAY?
Bibliographic record
Abstract
Abstract. Although maps have been around for a very long time, web maps are yet very young in their origin. Despite their relatively short history, web maps have been developing very rapidly over the past few decades. The use, users and usability of web maps have rapidly expanded along with developments in web technologies and new ways of mapping. In the process of these developments, the terms and terminology surrounding web mapping have also changed and evolved, often relating to the new technologies or new uses. Examples include web mapping, web GIS, cloud mapping, internet mapping, internet GIS, geoweb, map mashup, online mapping etc., not to mention those with prefixes such as “web-based” and “internet-based”. So, how do we keep track of these terms, relate them to each other and have common understandings of their meanings so that references to them are not ambiguous, misunderstood or even different? This paper explores the terms surrounding web mapping and web GIS, and the development of their meaning over time. The paper then suggests the current context in which these terms are used and provides meanings that may assist in better understanding and communicating using these terms in the future.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.012 | 0.022 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.006 |
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".