Our Cultural Commonwealth: The report of the American Council of Learned Societies Commission on Cyberinfrastructure for the Humanities and Social Sciences
Bibliographic record
Abstract
"Cyberinfrastructure" is more than just hardware and software, more than bigger computer boxes and wider pipes and wires connecting them. The term was coined by NSF to describe the new research environments in which high-performance computing tools are available to researchers in a shared network environment. These tools and environments are being built, and the ACLS feels it is important for the humanities and social sciences to participate in their design and construction. Of course, scholarship already has an infrastructure: that infrastructure consists of the libraries, archives, and museums that preserve information; the bibliographies, finding aids, and concordances that make that information retrievable; the journals and university presses that distribute the information; and the editors, librarians, archivists, and curators who link the operation of this structure to the scholars who use it. This infrastructure was built over centuries, with the active participation of scholars. \n \nCyberinfrastructure is being built much more quickly, and so it is especially important that humanists and social scientists actively engage with it, articulate what they require of it, and contribute their expertise to its development. "Our Cultural Commonwealth: The final report of the American Council of Learned Societies Commission on Cyberinfrastructure for the Humanities & Social Sciences" presents an agenda for that engagement.
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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.018 | 0.007 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.011 | 0.016 |
| Insufficient payload (model declined to judge) | 0.048 | 0.015 |
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".