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.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".