Embracing throwntogetherness: Unravelling the relational dynamics of borderlands in divided cities
Bibliographic record
Abstract
This research delves into the relational character of borderlands examining the nuanced concept of throwntogetherness within contested cities. Whilst prevailing literature often characterises walls and borders as static entities, this study proposes a paradigm shift, advocating for the recognition of borderlands as complex and relational networks. Using a mixed-method research approach, the study examines the profound ramifications of divisions, addresses socio-spatial imbalances, and hidden interconnected human experiences within divided landscapes. Through a comprehensive analysis of the city of Belfast, this research uncovers three interrelated patterns that reflect the spectrum of in-betweenness within interface areas encompassing separation, openness, and seclusion. These patterns extend beyond local geography, revealing broader socio-spatial phenomena. The findings highlight interfaces as junctions of both connection and division, prompting inquiries into effective urban planning and design strategies that foster meaningful relationships and cohesive urban dynamics. By dissecting socio-spatial dynamics that exist in these contested environments, this research unveils the array of tensions and synergies embedded in throwntogetherness, intricately woven into the urban fabric of divided cities. • This research challenges the static view of borderlands and advocates for seeing them as relational spaces. • Three interrelated patterns of in-betweenness are identified —separation, openness, and seclusion— revealing the complexities of divided urban areas. • The findings raise questions about urban planning strategies to promote meaningful relationships and cohesion in bordering regions. • This study contributes to a deeper understanding of the complexities surrounding border areas and offers insights into cultivating coexistence within such environments
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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".