Digital rurality: A three-fold model and research agenda
Bibliographic record
Abstract
Rural spaces are increasingly shaped by the pervasive influence of digital technologies, which are integral to their constitution, experience, and practices. This article distills how the forces of digitality inflect contemporary conceptualizations of rurality by iterating Halfacree's (2006, 2007) model of rural space to account for the emergence of what we term digital rurality . We advance a threefold heuristic of digital rurality as comprised of digital rural localities , formal representations of the digital rural , and everyday lives of the digital rural . Mobilizing this framework, we identify that digital rural spatialities are produced via the transformation of socio-spatial relations within rural localities; the representation of rural spaces by dominant actors staking claims about digital technologies; and everyday engagements with rural spaces mediated via digital technologies. We argue that an emergent digital ruralities research agenda must examine the power relations shaping digital ruralities, and geographic differences in how digital ruralities manifest between the Global North and South. • Rural studies have yet to account for the role of digitality in shaping rurality. • Digital technologies are not only drivers of change but constitutive of rurality itself. • Digital rurality designates rural relations shaped by forces of digital mediation. • Digital rurality is produced through localities, representations, and practices. • Future digital rural research must engage with questions of power and spatiality.
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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.006 | 0.039 |
| Scholarly communication | 0.024 | 0.038 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.008 | 0.008 |
| 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".