Approvals, Slips, and DDA! Oh My! The Yellow Brick Road to Collaborative Approval and DDA Profiling
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
In the last several years, approval profiling has changed significantly and grown increasingly complex, particularly due to the prevalent shift toward collecting in electronic formats. While approval profiles have been predominantly e-preferred for some time, the growth of demand-driven acquisition (DDA) has led to new license models, modes of acquisition, and tighter integration of DDA with approvals. With the advent of the DDA-preferred approval plan came options for the inclusion of multiple e-book platforms as well as complexities involving publisher embargoes. Additionally, the numerous approval and DDA profile parameters, workflow options, and administrator settings vary widely, resulting in a seemingly endless array of possibilities that can affect how titles are ultimately profiled. The task of creating a new profile or preparing profile reviews can be overwhelming, especially for those new to profiling or trying a new vendor. However, it can and should be a collaborative experience with vendors that leads to more than just great profiles. While library staff should strive to learn how to make the most of what a vendor offers, vendors should inquire about the library’s collection development strategies, issues, and needs. Vendors can also share current trends and offer advice modeled on how other libraries handle similar issues, as well as gather feedback for potential development. This paper supplies tips that will help library staff who are preparing to create or review approval or DDA profiles or to profile with new vendors, to be better prepared in order to maximize their time profiling with vendors.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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