Digital technologies and environmental change : examining the influence of social practices and public policies
Bibliographic record
Abstract
Digital technologies and services affect the planet. Every line of code, every photo uploaded to ‘the cloud’, and every smartphone, computer, or ‘IoT’ device, has an environmental footprint. That footprint often takes the form of carbon dioxide emissions, ecosystem degradation, and resource depletion, each of which occur to varying degrees throughout the production, use, and end-of-life processing of digital technologies. In the past two decades, multidisciplinary researchers and practitioners have examined and responded to many aspects of these processes; the responses have taken diverse forms, including energy efficiency and design standards, restrictions on the use of hazardous materials, and the international adoption of rules and regulations about electronics waste (e-waste). Despite these responses, the global environmental footprint of digital technologies and services has continued to rise due to their growing ubiquity, dwindling lifespans, and ‘always on’ support infrastructure. In this thesis, I respond directly to calls for increased analysis and discussion of the social practices and public policies that influence the environmental footprint of digital technologies (e.g. [83, 139, 232]). To narrow the scope of this broad line of enquiry, I focus on the social practices of retrocomputing repairers and human-computer interaction (HCI) academics— two communities whose practices influence the footprint of digital technologies—as well as environmental public policies that influence HCI practitioners. By drawing on secondary data and semi-structured qualitative interviews with 7 retrocomputing repairers and 22 HCI academics, my thesis offers three sets of contributions to complementary and ongoing conversations within the HCI community. The first two sets of contributions focus on the social practices of distinct but indirectly connected communities: retrocomputing repairers and HCI academics. While many HCI academics work to conceive of new digital products and services, the retro repairers actively work to maintain their ageing digital products in the face of increasingly scarce resources. Each group influences the environmental footprint in unique ways, which have been hitherto unexplored. I discuss some of these influences, and use them to highlight questions about existing and future HCI research. The third set of contributions focuses on environmental public policies and HCI. By fusing two existing approaches to understanding public policies and their relevance to HCI, I highlight opportunities for HCI researchers to engage with and influence environmental public policy. This allows me to suggest ways that environmental public policy could influence and be influenced by HCI 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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".