Redesigning Workflows and Implementing Demand-Driven Acquisition at Virginia Tech: One Year Later
Bibliographic record
Abstract
Library budgets are often stagnating, staff time is being redirected towards other needs, and demand for online resources is seemingly insatiable. These realities were part of the impetus behind Virginia Tech Libraries’ decision to begin a one-year demand-driven acquisitions (DDA) pilot program. This paper provides an overview of the DDA implementation challenges at Virginia Tech’s University Libraries and will detail the collection opportunities and financial benefits gained. Our goal is to provide participants with information to assist with their implementation of DDA. In summer 2012, Virginia Tech implemented a multivendor DDA option with YBP Library Service. The implementation and integration of DDA was not a one-step process. We continue to assess our workflow to meet the challenges of integrating DDA with our discovery layer Summon, managing cost, and addressing access problems. In our study, we compared cost and usage data from our 2010 and 2011 approvals and firm orders, COUNTER BR1 reports, and other vendor-provided data.
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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".