Bibliographic record
Abstract
comptes-rendus sur les ressources numériques 357 welcome addition.The fact that it is possible to move files into a different environment does bring the possibility of carrying out the analysis in several steps using different platforms-something that Social Media Lab clearly did not intend or encourage as the main use of its platform, since an import option is not available for parsed datasets.The one thing that I was left wondering about is if there were plans to include more robust options for automatic clustering, such as topic modelling.However, the overhead and computational expense incurred when modelling complex networks of online conversations could bring additional complications.In this sense, Social Media Lab has been practical in setting the fine line on what its tool aims to accomplish, which is not an easy thing to do.Netlytic is a well-designed framework that succeeds in the extraction and analysis of social networks from online conversations.The framework encourages researchers to hit the ground running and skip the inconveniences and difficulties that parsing a dataset can bring.On top of that, the interfaces are well designed and its processes well documented.I found that Netlytic is an excellent addition to the ever-expanding digital humanities toolbox.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.423 | 0.413 |
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".