Monitoring Responding to Information Ecosystem Incidents: A Conceptual Framework Understanding Information Ecosystem Risk and Leveraging Academic Expertise for Information Incident Response
Bibliographic record
Abstract
Our digital information ecosystem shapes the flow of information, the spread of disinformation, the power of adversarial actors, truth, trust, and democracy itself. Characteristics of this ecosystem impact crisis prevention, mitigation, preparedness and response; yet, research on the intersection between digital information ecosystems and crisis and emergency management is scarce. The capacity to deliver and respond to incidents in the information ecosystem is crucial in combating emerging digital threats, such as foreign influence, misuse of generative AI, and online extremism. This paper addresses these challenges by defining the digital information ecosystem and information incidents as they relate to crisis and emergency management and provides an interdisciplinary information incident response framework that integrates conceptual academic research with practitioner perspectives for improved crisis response.
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.008 |
| Science and technology studies | 0.005 | 0.032 |
| Scholarly communication | 0.022 | 0.031 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".