Critical Incidents Analysis: mismatching expectations and reconciling visions in intercultural encounters
Bibliographic record
Abstract
Conflicts among stakeholders are common in Community Informatics (CI) research. They often derive from mismatches of expectations and are exacerbated by communication and intercultural issues. Such mismatches are breaking points that might compromise the relationship of trust among project stakeholders and, ultimately, project outcomes. In CI, reflecting on moments of conflict and mismatch might help researchers attend to assumptions and interpret aspects of the cultural context of the communities they work with, as well as their own. This reflection should, then, contribute to a closer connection among stakeholders and sustainable project outcomes. In this paper, we present the Critical Incidents Analysis (CIA) Framework (Brunello, 2015), a tool that was conceived within the Community and Development Informatics field with the aim to reflect upon incidents and misunderstandings among stakeholders, their different cultural perspectives, and – eventually – deal with project breakdowns. We apply the framework to our own research where we analyse conflicts and mismatches of expectations that arose during the fieldwork conducted by two of the authors. We conclude that the CIA framework, applied “a posteriori” to our cases, was a useful tool to better analyse and report on our research, and to recast incidents as opportunities to enable a deeper understanding and – in some cases – build trust among stakeholders.
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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 | 0.041 | 0.114 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.012 | 0.020 |
| Scholarly communication | 0.019 | 0.020 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.004 | 0.005 |
| 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".