Sustainability powered by digitalization? (Re-)politicizing the debate
Bibliographic record
Abstract
As ecological crises escalate, various stakeholders frame digitalization as a key solution for sustainability transformations. Besides incremental optimization, this promise has not materialized yet. We argue that digital solutions toward sustainability objectives are shaped by and reinforce power structures that effectively undermine sustainability outcomes. Academic discourse and governance are often dominated by a technology-centric framing in contrast to technologically informed, power-centric approaches. In this article, we develop an interdisciplinary framework to analyze three interconnected dimensions of power at the sustainability-digitalization-nexus and reveal how they obstruct sustainability. We locate power at the levels of environmental knowledge, governance, and technological materiality. First, digital technologies create representations of the environment that reinforce, reconfigure, or clash with preexisting ones, striving for more and better digital real-time data for technological control. Second, the spread of digital technologies is facilitated by emerging actor coalitions that promote digitalization while employing a reductionist understanding of sustainability. This narrows the policy space to optimization and incremental solutionism, which reproduces the status quo. Finally, the designs and material infrastructures of current digital technologies create path dependencies and lock-in effects while the underlying colonial resource and wealth flows remain hidden. We advocate for a (re-)politicization of digitalization across these dimensions to leverage its potential for sustainability transformations. We conclude that digitalization cannot spare us from political conflicts and deliberation processes about desirable sustainability futures. The debate should re-center fundamental questions about what kind of sustainable futures we want, where technology has a role to play, and where it does not.
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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 | 0.017 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.038 |
| Scholarly communication | 0.018 | 0.029 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".