Dispersed/Networked Open Social Discovery Research: Applications for Humanistic Machine Learning & Topic Modelling
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
One of the benefits of open social scholarship also presents researchers with a challenge: the dispersed nature of the knowledge breakthroughs presented by a diverse network of scholars inside and outside of the academy. Accessibility enhances the broad reach of open social scholarship, leading to a democratic engagement across a culturally rich spectrum of participants. But such processes do not necessarily provide coherent critical constellations or knowledge clusters from the perspective of the broad audience. Further, due to the positive benefits of functioning as a group, open social scholarship groups may ignore or simply not register potential discovery research breakthroughs that do not meet the criteria for the groups’ success. In all three instances (knowledge dispersal; lack of knowledge development coherence for all of the community and non-community members across a network; parallel knowledge breakthroughs that remain dispersed/unrecognized), machine learning and topic modelling can provide a methodology for recognizing and understanding open social knowledge creation.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it