Analysing Urban Community Informatics from a Resilience Perspective
Bibliographic record
Abstract
The concept of resilience – the capacity to cope with, adjust to and potentially transform amid change and uncertainty – is of increasing interest and activity within community development. It therefore presents a new lens that may be used to understand and guide community informatics. Yet resilience is a concept that has often been well-understood but poorly-applied or, when applied, has been poorly-understood. The purpose of this paper is therefore to develop a well-conceptualised model of resilience that can be applied in both community informatics research and practice. The model presented here sees communities as systems and resilience as a set of foundational and enabling system sub-properties. It is used as the basis for analysing ICT interventions in urban communities; showing how ICTs largely strengthen community resilience but may also weaken some aspects. The paper demonstrates the viability of the developed resilience model to both understand and evaluate community informatics interventions, and it argues that this provides a broader and deeper understanding of those interventions than other perspectives can offer. Further practical application of the model is, however, required.
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 | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| 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".