An assessment of the accessibility of spatial data from the Internet to facilitate further participation with geographical information systems for novice indigenous users in South Australia
Bibliographic record
Abstract
Geographical Information Systems (GIS) are becoming more widespread, but there is still debate as to whether the use of GIS is a socially inclusive activity and, within this debate, the accessibility of spatial data is a pertinent issue. Governments around the world have developed Spatial Data Infrastructures (SDI) to facilitate the dissemination of spatial data and Australia, Canada and the United States are acknowledged leaders in this field. Such initiatives have the potential to bring new users, for example Indigenous people, to GIS but with this expansion there is a consequential demand for up-to-date and freely available spatial data. This paper evaluates the accessibility of spatial data from the Internet within Australia and contextualises performance with a comparison with Canada and the United States. The GIS industry has a variety of terms for data and this research uses the most popular of these to search the Internet for webpages containing spatial data. A traditional information retrieval technique (Precision) is used to analyse the webpages returned, and is supplemented with further analyses regarding webpage ranking and industry sector. The main findings indicate approximately 50% of retrieved webpages contained free data, but reveal that Australia performs worse than North America in comparison. The highest ranking websites are Canadian, and governments are the dominant spatial data providers. This research indicates that Australian data is not easily accessible and, if the public are to be more engaged, the delivery options should be reviewed.
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How this classification was reachedexpand
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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.006 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".