Shaping the Future: A Research Agenda for U.K. Libraries
Bibliographic record
Abstract
Objective – This study explored current and future trends in librarianship within the U.K. library and information profession, intending to highlight the most critical for future evidence based research. Research outcomes should resonate across the wider sector and be an indicative stepping stone to collaborative research endeavours by members of the profession at a time when funding is tight, and staff availability is in short supply. Methods – A qualitative Delphi consensus method was chosen for the research, adapted from Paul’s (2008) modified Delphi card-sorting model. Contributions from conference programs and 31 individual experts from the U.K. library and information profession contributed to the generation of current themes and trends impacting their library environments. Data were analyzed by the experts in an incremental manner following the adapted methodology, and consensus was achieved through the process. Results – The findings of the research indicated that there were five significant trends and areas of concern which are impacting our libraries at all levels. These naturally include pressing current concerns such as the impact of artificial intelligence (AI), critical librarianship, and censorship/book banning. Library spaces remain a significant issue for the wider sector. Conclusion – The adapted modified Delphi card-sorting method with three distinct sections to the research proved especially valuable in a study where there were many different approaches to librarianship. The use of conference data to seed the initial set of themes has been shown to be unusual and rarely used in this way before. The process of achieving and reaching consensus illustrated the need for the profession as a whole to work more closely together. The outcome of the consensus research should now be taken forward collaboratively by the library profession, with space and training given to staff across all sectors and grades to engage in evidence based research for the benefit of all.
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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 | 0.056 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.033 | 0.023 |
| Scholarly communication | 0.056 | 0.074 |
| Open science | 0.006 | 0.035 |
| Research integrity | 0.018 | 0.020 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".