How librarians make decisions: the interplay of subjective and quantitative factors in the cancellation of Big Deals
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
Purpose The purpose of this study is to investigate the decision-making process of librarians at the University of Western Ontario who attempted to cancel the Wiley Big Deal. The aim of the study is to reveal the underlying factors that affected their decision-making process. By understanding the decision-making process of librarians, it may be possible to devise a system that takes into consideration not only quantitative factors but also the subjective or qualitative factors that impact librarians’ decisions and thus make it easier to cancel these Big Deals. Design/methodology/approach The study involved administering an online survey to 25 librarians involved in the cancellation project. Follow-up interviews were conducted with 13 of these librarians to understand at a deeper and more nuanced level the factors that influenced their decisions. Findings The main finding was that the librarians who participated in the study could be divided into two groups – a data-driven criteria group and a subjective criteria group – based on their ranking of the factors used to make their cancellation decisions. Most librarians interviewed used a mixture of quantitative factors and qualitative factors when making their cancellation decisions. The authors found that those participants who had greater professional experience and a closer relationship with the faculties in their subject areas had more difficulty in cancelling journals. Very few librarians relied on quantitative data alone. Originality/value This study is one of few that have examined the subjective factors that influence librarians’ decisions regarding cancellation of Big Deals. It has implications regarding the movement towards centralized collection management and reliance on quantitative data alone when making collection decisions.
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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 | 0.000 | 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