Evaluating the socio-economic impact of Geographic Information: A classification of the literature
Bibliographic record
Abstract
Geographic information (GI) is increasingly important to citizens, businesses and governments in modern societies. Considerable effort has been devoted to developing our understanding how GI affects the information management strategies and practices of individual organizations (GITA, 2006). However, there is an increasing awareness across public and private organizations that more attention has to be paid to assessing the broader economic and socio-economic impacts of GI technologies (European Commission, 2006; Craglia and Nowak, 2006). Given the investments that local, regional, national and supra-national organizations have made in GI and may consider for the future, it is imperative that the return on investments in GI be assessed across all scales. This is particularly relevant as GI is viewed increasingly as an infrastructural element for which investments and benefits must be justified and quantified (Grus, 2007). Although an increasing number of researchers are examining different approaches to evaluating specific GI applications, it is clear that the documentation of business cases and assessment strategies for GI investments is still incomplete (GITA, 2006). The key objective of this paper is to summarize and synthesize some of the current literature related to assessing the value of GI. This review, which was conducted under the auspices of the EcoGeo II project (http://ecogeo.scg.ulaval.ca), is based on an examination of 44 academic, business and government studies. A classification framework was constructed to compare these studies with reference to two key variables: topics and approaches. The studies we analyzed were developed within different public and private organizations and spanned international, national and regional scales. To make the study manageable, we focused particularly on the regional context represented by Canada’s province of Quebec. The results show that the topic of assessing the impacts of GI remains largely embryonic in nature. In particular, we identify the lack of a common vocabulary, no shared understanding concerning exactly which topics should be assessed, a lack of testing for any approaches suggested for evaluation, and often a dearth of concrete answers.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.052 | 0.058 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".