Lessons Learned: The Multifaceted Field of (Digital) Neighborhood Development
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In a cross-national project, 14 neighborhoods from Germany, Austria and Switzerland were accompanied on their way to digitally supported neighborhood work. This paper discusses general requirements, choosing a suitable digital tool, the implementation process as well as the challenges faced by the various stakeholders. The following factors have been found to play a major role in sustainable neighborhood work: good fit with overall development strategy, interplay between online neighborhood work and physical interactions, strong existing neighborhood management structures, strategic planning of digitalization activities, start-up funding for innovation activities, and above all, the presence of a committed person or team as well as interesting content to attract users. Depending on the neighborhood, self-managed and individualistic solutions are preferred to generic and/or commercial solutions. There is no ‘fit-for-all’ path to sustainable digitally supported neighborhoods.
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.
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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it