Bibliographic record
Abstract
this report provide an important first step in describing users, uses, and impacts from electronic and networked services. The report could not have been completed successfully without the assistance of a number of individuals and organizations. First, the study team wishes to acknowledge the 24 participating member organizations that have supported the project: At each of these libraries, liaisons and other individuals assisted the study team by providing input and suggestions to earlier drafts, by field-testing the statistics and measures, and by attending a number of meetings and discussions regarding the content of the report. We are deeply indebted to these organizations, the directors, the liaisons, and others at those organizations that participated directly in the project. We also want to acknowledge the participation and assistance from a number of data base vendors and providers who participated in various meetings and discussions to help develop operational definitions procedures for these statistics and measures. These include: Academic Press/IDEAL Bell & Howell EBSCO Elsevier/ScienceDirect Gale Group JSTOR Lexis-Nexis netLibrary OCLC/FirstSearch Ovid SilverPlatter University of Alberta Auburn University University of Connecticut University of Illinois-Chicago University of Maryland-College Park University of Nebraska-Lincoln University of Pennsylvania University of Pittsburgh University of Southern California Virginia Polytechnic Institute and State University University of Wisconsin-Madison Library of Congress Arizona State University University of Chicago Cornell University University of Manitoba University of Massachusetts University of Notre Dame Pennsylvania State University Purdue University Texas A&M University University of Western Ontario Yale Univ...
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.390 | 0.242 |
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; the direct Gemma label and the distilled Codex classifier 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".