FOUNDATION OF SUCCESS: EVALUATING THE ARCTIC-SDI
Bibliographic record
Abstract
The Arctic covers 30 million km2 which encompasses eight countries and has a population of over 4 million people. With datasets produced by private and public stakeholders all over the world and noted gaps in data for many parts of the region, there is an opportunity to collaborate and create a unified Spatial Data Infrastructure (SDI) for the Arctic. This research identified the criteria for evaluating the long-term efficacy of the Arctic-SDI; development of the tool early in the SDI development (4th year of operations), meant that we could apply the assessment tool to inform next-stage planning. In this short-term evaluation, half the countries were found to be strong contributors, more-or-less equally contributing in terms of deliverables, resources and leadership to the Arctic-SDI. Half the countries contributed noticeable less due to inability to meet deliverables in a timely and useful manner, less participation in working groups and little or no resource contributions. Semi-structured interviews were also conducted to obtain internal participant view of the Arctic-SDI collaboration success and shortcomings. The interviewees identified opportunities, limitations and risks that they perceived as internal participants of the Arctic-SDI. Most of the issues associated with opportunities, limitations and risks could be cross-validated with the external matrix themes of resource, deliverable and participation; however, the importance of communications was strongly emphasized in the interviews and not represented in the external evaluation matrix developed from SDI-evaluation literature. The completion of both the external (matrix) and internal (interviews) evaluations led to the multi-view framework that can be used to assess the long-term potential of the Arctic-SDI. The evaluation tool can also be used for defining tasks and clarity of responsibilities for the next 5-year MoU (2019-2024) and/or to assess the Arctic-SDI to identify challenges and mitigation measures that would assist in longevity. This tool can also be used for other regional SDIs to define MoUs and assess the potential for success.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| 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 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".