Frameworks and Models for Disseminating Curated Research Outcomes to the Public
Bibliographic record
Abstract
In our post-truth society, mobilizing “facts” and “evidence” has never been more important. We live in an age that is paradoxically information rich due to the proliferation of Internet Communication Technologies (ICTs) and information poor due to the spread of misinformation. Academic research outcomes are traditionally disseminated via peer-reviewed publications, conference presentations, and in the classroom; however, this research is not often effectively communicated to both decision makers and the general public(s). There is no perfect way of disseminating research outcomes; however, there are lessons to be learned from curatorial and communication frameworks developed in museums as these institutions have a long history educating and engaging the public. This article explores the new concept of “research curation,” or rather the enhanced dissemination of curated research outcomes to reach diverse audiences. Closing the “gap” between academia and the public is essential for increasing civic literacy around issues that threaten sustainability. By adapting curatorial and communication methods developed in museums along with ICT models, the practice of “research curation” can be an effective framework for improved dissemination of academic knowledge.
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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 | 0.209 | 0.216 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.017 | 0.010 |
| Science and technology studies | 0.008 | 0.038 |
| Scholarly communication | 0.034 | 0.036 |
| Open science | 0.010 | 0.018 |
| Research integrity | 0.015 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".