Bibliographic record
Abstract
Academic libraries received numerous free offers during the COVID-19 pandemic. Existing business literature suggests that there are benefits and costs associated with free offers for both the businesses that provide them and their customers. This study analyzes the free offers received during a three-month period at the beginning of the pandemic in 2020. The author monitored direct offers from vendors, LIBLICENSE-L@LISTSERV.CRL.EDU, information obtained from peers, and publicly available data from the International Coalition of Library Consortia (ICOLC). The offers that would normally require paid institutional subscriptions were included in the study. Databases were the largest offer category (41%), followed-by e-books (20%). Most (76%) required registration by library representatives, allowing vendors to track usage data. Only a small portion (8%) of these free offers was already held at the study site, Penn State University Libraries (PSUL). The implication might be that most of the offers were either new, not high-priority or not affordable for PSUL. The findings of this study suggest free offers provide intangible value for both libraries and vendors that cannot be measured through cost-per-use data analysis. For example, libraries gained opportunities to trial new products without any risk, temporarily expand their collections, and help users during the crisis when access to the library buildings was disrupted. Vendors increased product visibility, gained customer information and usage data, identified potential customers, and created goodwill with the library community. This study is relevant to business librarianship not only because these free offers included business and related disciplines but also because some business librarians engage with vendor relations and need to understand different business models including free offers.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".