Redesigning Workflows and Implementing Demand-Driven Acquisition at Virginia Tech: One Year Later
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 it