The University of British Columbia Data Library: An Overview LAINE G.M. RUUS
Bibliographic record
Abstract
a unique organizational model among data libraries (by which I mean to include, as well, data archives and data banks) in the manner in which it is jointly operated by the library and Computing Centre of the university. How it came to be as it is is a result of its historical development; it continues to function as it does due to the success of the original model. In 1963/64, a Statistical Centre for the Social Sciences was established in the university’s Faculty of Arts, primarily through the efforts of the departments of economics, political science, and anthropology and sociology. The purpose of the center was to provide statistical and programming consultation to faculty and graduate students in the Faculty of Arts, i.e., to act as an intermediary between the social scientists and the Computer Centre. By 1965 the Statistical Centre hadentered into membership agreements with the, then, Inter-University Consortium for Political Research (ICPR) and the International Survey Library Association (ISLA), the membership arm of the Roper Public
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.038 | 0.083 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.010 |
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".