The Emergence of the Placial-Technical: Digital Placemaking as Information Practice
Bibliographic record
Abstract
In an era of ubiquitous computing and generative AI, our experience of place is increasingly mediated by digital technologies, creating hybrid environments where physical and virtual interactions converge. While fields like media and urban studies have explored this through the concept of ‘digital placemaking’—the use of digital media to create a sense of place—this phenomenon has received limited attention within information studies. This paper addresses this gap by proposing a new conceptual framework, termed the ‘placial-technical,’ which refines the traditional socio-technical perspective to specifically analyze the mutual shaping of place, information, and technology. Using this lens, we argue that digital placemaking should be understood as a form of information practice, encompassing the socially situated ways individuals seek, use, and share information to construct meaning about their surroundings. Drawing on literature from human geography, media studies, and Human Computer Interaction, we trace the evolution of placemaking concepts and technologies. We then analyze digital placemaking through the dual processes of perception (how information inputs shape our understanding of place) and representation (how we create informational outputs to depict place), focusing on the growing influence of algorithms and generative AI. This synthesis reveals research gaps and offers implications for information studies. By conceptualizing digital placemaking as an information practice, the field can extend its theoretical and methodological tools while informing the ethical design of technologies that foster authentic community engagement and place attachment in a digitally mediated world.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.008 | 0.124 |
| Scholarly communication | 0.020 | 0.026 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".