Bibliographic record
Abstract
Geographic information systems were first formally introduced with the development of the Canada Geographic Information System (CGIS) in the early 1960s. The CGIS both benefitted from earlier conceptualizations and pioneered new software, hardware, and algorithms for handling spatial data. From the 1970s to the 1990s there was an emphasis on geographic information systems . GIS innovators John Coppock and David Rhind divided this era into: (i) a pioneer period from the late 1950s to the 1970s which emphasized conceptual and software development; (ii) a government‐funded experimental research period from the mid‐1970s to the early 1980s with GIS continuing to be provided on mainframe computers; (iii) a commercial period with increased involvement of both industry and government from the early 1980s to the late 1980s dominated by companies such as Esri and Intergraph; and (iv) the end of the 1980s to the mid‐1990s showing ever‐increasing academic participation together with intense vendor competition that resulted in a reduced number of GIS companies that were now producing GIS with user‐friendly interfaces on desktop computers. Also included in this period is a discussion of the impact of Michael Goodchild's seminal paper on geographic information science . This captured the imagination of the community, and journals, academic teaching, and research have all reflected this change to the present day. Two further periods are added to the Coppock–Rhind schema: (v) an account of the rise of GIScience, the reactions to its dominance, which included the introduction of public‐participation GIS, and the growth of the University Consortium for Geographic Information Science. This period covers the mid‐1990s to 2005. And, finally, (vi) the period from mid‐2005 to the present, which covers the worlds of volunteered GIS, web‐based mapping, mobile GIS, cloud computing, and big data. The conclusion provides a discussion of the extent to which GIS has returned to its roots in geography.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.024 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.081 | 0.037 |
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".