Green IS Research: A Modernity Perspective
Bibliographic record
Abstract
Over the past two decades, the information systems community has become engaged in improving the environmental effects of information systems and technologies, which has given rise to the green IS field. Despite increasing interest, some have suggested that progress toward meaningful solutions for sustainability has been too slow. Responding to these concerns, we examine the development of green IS research using the modernity perspective to understand green IS’s evolution and to present alternative perspectives to motivate future research. From a sample of over 80 green IS papers published over a 15-year period, we identify four main patterns of modernity that are manifest in green IS research. These patterns include the importance of the individual in solving environmental problems; science as the main source of solutions; and the emergence of an artificial science approach, reliance on technology, and growth as businesses’ ultimate goals. Further, our analysis reveals that green IS research has started to demonstrate elements of a hyper-modernity perspective that emphasizes reflexivity. We argue that future green IS research should continue on this path and propose a conceptual framework inspired by hyper-modernity and centered on reflexivity that could serve as a guide for future research.
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 | 0.018 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.005 | 0.057 |
| Scholarly communication | 0.016 | 0.026 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".