Primer on policy implications of cloud computing
Bibliographic record
Abstract
This guide is one in a series of Operational Policy documents being developed by GeoConnections. This guide is intended to inform CGDI stakeholders about the nature and scope of cloud computing and the realities, challenges and good practices of related operational policies. Cloud computing provides flexible, location-independent access to computing resources that are quickly and seamlessly allocated or released in response to demand. Computing clouds provide computation, software, data access, and storage resources without requiring cloud users to know the details of the computing infrastructure. For geospatial data and software providers, cloud computing represents a potential new way of doing business, by providing lower cost or free options for clients to access products and services online. Rather than acquiring software for in-house implementation and downloading complete databases, clients can "rent" the software and access only the data they need through web services, on an as-required basis. The "cloud" is poised to become the accepted place for a broader range of relatively unsophisticated users of geospatial data to access and use this powerful technology. Moving to the cloud seems inevitable. A Cloud Computing Roadmap is an integral component of the Government of Canada's information technology shared services (ITSS) model (Danek, 2010). Shared Services Canada is responsible for the delivery of certain IT services on behalf of all government departments, including data centre management. At the provincial level, at least two governments are assessing cloud computing (CC). The Government of Ontario is exploring the potential of CC as a better way of using and delivering online services (Microsoft, 2011). And in its IM/IT strategy document, the Government of British Columbia identifies the leveraging of CC services as one of two key IT/IM hosting strategies for the province (Office of the Chief Information Officer, 2011). In an international example, the US federal government has introduced a "cloud-first" policy for new government computing solutions (Zients, 2010). American adoption estimates across all sectors peg growth in spending for managed cloud services at $14B by 2014 compared with $3B in Feb 2011. According to a Financial Times article [http://www.ft.com/cms/s/0/934dcf92-703d-11e0-bea7-00144feabdc0.html] in May 2011, the global value of the cloud sector could reach $150B by 2014. Other estimates differ, but all agree that cloud computing is becoming a significant business. A growing number of organizations already on the cloud further reflect this trend. This guide introduces key issues in geospatial operational policy, imperative to the success of any venture into cloud computing. Operational policies are the guidelines, directives and policies that an organization employs to address the life cycle of geospatial data (i.e., collection, management, dissemination and use). This guide will be of interest to anyone seeking a better understanding of cloud computing and areas of related operational policy, such as liability, privacy and confidentiality, security, licensing, copyright, archiving, regulations and standards.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.002 | 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".