Building Partnerships Between Social Science Data Archives and Institutional Repositories
Bibliographic record
Abstract
The Data-PASS partnership engages in collaboration at three levels: coordinated operations, development of best practices, and creation and use of open-source shared infrastructure. The first talk in the session provides an update on our search for replication and distributed storage technologies for preservation. Systems like iRODS and LOCKSS can be developed into preservation environments for social science data archives. The key when implementing these preservation environments will be the modification of existing archive policies and procedures to reflect new dependence on collaboration. The second talk discusses the collection of international public opinion data collected by the USIA, which began in 1952 and extended through 1999. Until recently, these data were difficult to access. The Roper Center and the National Archives and Records Administration have identified, rescued, and made these data available to the research community. The third talk describes a new alliance between ICPSR and Institutional Repositories (IRs) with the goal of preserving and re-using social science data. This talk focuses on the formation of these partnerships; how an archiving guide for IRs will be developed; and new services that ICPSR can offer to IRs to assist with social science data. The fourth talk summarizes the efforts of ICPSR and the Roper Center to migrate punched card data to modern preservation formats. This presentation focuses on the recovery of the Cornell Retirement Study, a longitudinal study that began in 1952. The final talk discusses the current collaborative structure of Data-PASS, our agreements, infrastructure, and the services and infrastructure available to new partners.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.014 | 0.022 |
| Open science | 0.008 | 0.016 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".