How the AIS can Improve its Contributions to the UN’s Sustainability Development Goals: Towards A Framework for Scaling Collaborations and Evaluating Impact
Bibliographic record
Abstract
In June, 2019, the Association for Information Systems (AIS) adopted a new approach to addressing global sustainability issues by establishing the AIS Sustainability Task Force (AIS STF). This initiative focuses on building on the outcomes from the United Nations (UN) Millennium Development Goals (MDG, 2000-2015) and applying them to address the challenges associated with the UN Sustainable Development Goals (SDG, 2016-2030). In this paper, we review the challenges and outcomes from the UN sustainability programs with their potential relevance to IS in general and the AIS in particular to inform and assist increased efforts to achieve the global sustainability goals. The initial event, the AIS Sustainability Summit held at ICIS 2019, provided a forum for AIS groups and communities to share their current interests, plans, activities, and experiences relevant to the MDG and SDG. The event primarily focused on facilitating opportunities to scale the AIS’s sustainability activities through multi-disciplinary collaboration across the AIS and its communities. Members from four AIS special interest groups and the STF’s Education Workgroup presented exemplary projects at the summit that demonstrated how one can apply applied IS and research capabilities to address sustainability challenges. The sustainability summit’s also explored opportunities to achieve positive impact in addressing the SDG’s global challenges through applying AIS members’ knowledge, skills, and capabilities in relevant ways in collaboration with suitable organizations outside the AIS. Potential organizations include business, government, societal groups, and UN bodies. We presented and discussed the AIS STF’s aims, plans, outcomes, and impact. By analyzing details and options for cross-organizational collaboration, the representatives of organizations at the sustainability summit developed a proposed framework for scaling contributions and evaluating impact. Finally, they drew conclusions about the proposed activities, approaches, and framework for the AIS to improve the scope and scale of its contributions in addressing the SDG. Critically, the AIS needs to ensure that its proposed activities, contributions, and impact are examined by an internationally recognized independent process. We propose a model for the AIS to realize this requirement for evaluation in 2021.
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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 | 0.125 | 0.151 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.021 | 0.020 |
| Science and technology studies | 0.019 | 0.032 |
| Scholarly communication | 0.063 | 0.086 |
| Open science | 0.007 | 0.052 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".