Information Intermediaries and Information Resilience: Working to Support Marginalised Groups
Bibliographic record
Abstract
ABSTRACT Information resilience has become a topic of interest to the information science community in recent years. The COVID‐19 pandemic has shone a light on the vulnerability of information and other networks and the impact on information providers and the information seekers who rely on them. In an exploratory study, we interviewed support workers who act as information intermediaries as part of their work roles about their experiences of providing information to vulnerable and marginalised people during the UK COVID‐19 lockdown. We present findings organised in three themes: shifting client information needs and support provisions, adjusting information sharing and communication practices and workarounds for physical information work. Throughout the themes, information resilience is evident as information intermediaries adapt their work practices to ensure they can continue to serve their clients. In this first stage of research our findings provide insight into the changes to information intermediaries' information behaviour and information work during a crisis, as well as the impact of these changes on the services they provide.
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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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 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".