Unbundling practice: the unbundling of big deal journal packages as an information practice
Bibliographic record
Abstract
Purpose The purpose of this article is to introduce a theoretical framework and approach for studying the evaluation and decision-making practices through which academic librarians attempt to reduce the cost of electronic journal subscriptions – an organizational practice known as the unbundling of big deal journal packages. Design/methodology/approach The article presents a literature-based conceptual analysis of several fields to delineate the elements of the practice of unbundling of big deal journal packages. Beyond analysing the prior literature, the discussion is supported by empirical findings from a pilot study on the topic conducted by two of the article's authors. Findings The main finding of the article is that the unbundling of big deal packages is a case of what sociologist refers to as decision-making in a social context. By reviewing previous studies, the article identifies the social and material elements constitutive of this practice. This, in turn, allows to develop questions and concepts for future research on the topic and to position it as an area of inquiry within the field of information behaviour/practices. Originality/value The article is the first attempt to conceptualize the unbundling of big deal journal packages by highlighting its phenomenological status as a type of information practice. In addition, the article proposes a research approach for studying this type of information practice by drawing on insights from the information behaviour/practice literature and enriching them through practice theory contributions in organizational studies and sociology.
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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 | 0.049 | 0.115 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.010 | 0.031 |
| Scholarly communication | 0.025 | 0.026 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".