Telling scholarly stories: Translating research outcomes into multimedia stories for the purposes of dissemination
Bibliographic record
Abstract
Introduction: Universities have always focused on research, but the dissemination of research results beyond the scholarly community is often less of a priority (Armstrong, 2011) and poses serious challenges for scholars. Research is regularly published in books and in specialized scholarly journals, but both are expensive and often not readily available to the general public. The presentation of research papers at scholarly conferences is also problematic in that audiences tend to be limited to other scholars. Lack of ready access to upto-date research results means that individuals, communities and sometimes even government policy makers do not have the information they need for decision-making purposes. Moreover, students’ preoccupation with day-to-day studying and their focus on class-related work means they too are often unaware of advances in knowledge and the work professors do in their role as researchers in the academy. To address the challenge of making research results available to a wider audience, my Undergraduate Research Opportunity award focused on the creation of multimedia journalism stories for a research-focused website. Ryerson University School of Journalism professor
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.029 | 0.170 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.021 | 0.015 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".