Conceptualising the link between information systems and resilience: A developing country field study
Bibliographic record
Abstract
Abstract Resilience—the ability of systems to cope with external shocks and trends—is a topic of increasing interest to research and practice. That growing interest is reflected within information systems (IS), but a structured review of IS literature shows a number of knowledge gaps around the conceptual and empirical application of resilience. This paper investigates what the subdiscipline of information and communication technologies for development (ICT4D) can contribute; finding that it offers the IS discipline fresh insights that can be built into a new framework of resilience, and an arena within which this new framework can appropriately be field tested. Application of the resilience framework was undertaken through interviews and a survey in an urban community in Costa Rica; benchmarking both community resilience and “e‐resilience” (understood here as the contribution of ICTs to community resilience), and developing from these a set of action priorities. The paper reflects on what can be learned generally from this conceptualisation and operationalisation of resilience. It also reflects on what ICTs contribute to resilience in developing countries and on what this ICT4D‐based research specifically contributes to the identified IS knowledge gaps. This includes identification of a future research agenda on information systems and resilience.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".