Bibliographic record
Abstract
While intellectually stimulating, the conceptual debates found in academic articles can sometimes be difficult for lay readers to engage with. In looking to make them more accessible to a wider audience, some journals have begun to explore different modes of communication. RFG encourages authors to adapt popular articles for mainstream media, and has worked to expand video dissemination through the IQSOG program and its partnership with Xerfi Canal. The first of its kind for a management journal, RFG is now offering an article on the 2022 hyperloop project (Belinski et al., 2022) in comic strip format to facilitate understanding of the concepts discussed in the article. While the article was already presented to Xerfi Canal in 2023, its comic adaptation aims to get students thinking about the complex issues discussed in the article, such as sociomateriality and the relationship between myths and markets. More broadly, this project responds to RFG?s longstanding interest in comics as a vehicle for offering visual representations of objects of research (Grimand, 2022), and looks to expand the use of comics as a means of disseminating academic research.
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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.020 | 0.016 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.074 | 0.020 |
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".