Bibliographic record
Abstract
Computational approaches to archives present archivists and users with new ways of engaging with records and their provenance. Such approaches are particularly useful for scientific archives due to the collective and collaborative nature of modern scientific knowledge production. This article explores computational approaches to digitized fonds of scientists involved in the transdisciplinary scientific movement cybernetics through the Cybernetics Thought Collective: A History of Science and Technology Portal Project as a means to reveal the ways cyberneticians have developed concepts and debated ideas through the creation and exchange of correspondence and other records. The project has experimented with machine-learning and natural-language-processing tools to generate data from the materials in an effort to reveal connections between the cyberneticians and their correspondence. Cybernetics seeks to understand the human condition through experiments with machines, and, in a cybernetically inspired sense, so too do archivists seek to understand their archives through experiments with machines. Such explorations are important for documenting scientific thought collectives like cybernetics in a digital age.
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 | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.014 | 0.024 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".