The value chain approach to evaluate the economic impact of Geographic Information: towards a new visual tool
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Geographic Information (GI) is becoming more important everyday at all levels of soci-ety. GI has a central role in supporting economies, improving business effectiveness in the private sector, enabling more efficient governments, and increasing citizens ’ quality of life. Assessing the value of digital information products, services and infrastructures is particularly complex due to the specific characteristics of GI as a not- standard eco-nomic good (Krek and Frank, 2000) and the nature of the GI market itself (Krek, 2006). One promising assessment approach is the value chain: value is created step-by-step along the chain. Thus, pricing in a value chain serves to determine the way in which the value created for the end user is distributed among the contributors. In theory, the value chain is one of the most suitable approaches to assess GI. However, it is also one of the most complex one due to the number of variables connected to how GI is produced and used. Therefore, it is often impossible to determine a single and constant value to specific GI (Longhorn and Blakemore, 2008) and a concrete example of appli-cation of a formal economic analysis based on the value chain concept still does not exist (Genovese et al., 2008). The EcoGeo project, in its first phase, has developed a prototype computer tool named Socioscope, which provides cartography of the links existing between various public and private contributors (Plante, 2006). In EcoGeo’s second phase, Socioscope will be upgraded and the value chain of the geomatic sector in Quebec will be defined. The final goal of the project is an economic evaluation for a test-area inside the value chain: the ability to measure the GI economic value will pro-vide key decision support for both institutional and private sectors.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it