The Sustainability Imperative in Information Systems Research
Bibliographic record
Abstract
This paper reports on a panel discussion at the pre-ICIS 2015 Workshop on Green Information Systems on the current state and future perspectives of SIGGreen—the Association of Information Systems’ special interest group on green information systems—and of green information systems (green IS) research in general. Over the past years, IS scholars have made important contributions advancing our knowledge about how information systems can contribute to solving problems associated with the degradation of the natural environment. However, it would appear that many view green IS as just another research topic in the IS field and not a very important one at that. This is questionable because sustainability is too important to be relegated as a footnote in the greater scheme of things. We suggest that the IS community should embrace sustainability as a core research imperative and integrate sustainability-related dimensions to research in theory and method, in rigor and relevance, and in the areas one chooses to research. We provide some actionable recommendations on how we as IS researchers and, indeed, how the IS field could help society and business interests make the transition to a sustainable world.
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 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 | 0.105 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.008 | 0.075 |
| Scholarly communication | 0.025 | 0.045 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.010 | 0.017 |
| 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".