Our Future, Our Skills: Using Evidence to Drive Practice in Public Libraries
Bibliographic record
Abstract
Abstract Objective – The public library sector’s future prosperity is contingent upon a well-trained, experienced, and valued workforce. In a collaborative initiative, State Library Victoria (SLV) and the Public Libraries Victoria Network (PLVN) commissioned an in-depth research study to examine the skills requirements of staff across the State. The Our Future, Our Skills project sought to identify the range of skills used by public library staff today, to anticipate the range of skills that would be needed in five years’ time, and to present a skills gap analysis to inform future training and development strategies. Methods – The project encompassed qualitative and quantitative research activities: literature review and environmental scan, stakeholder interviews, focus groups and a workforce skills audit. The research populations were staff (Individual survey) and managers (Management survey) employed in 47 library services, including metropolitan, outer metropolitan and regional library services in Victoria. Results – The high response rate (45%) reflected the relevance of the study, with 1,334 individual and 77 management respondents. The data captured their views related to the value of their skillsets, both now and in five years’ time, and the perceived levels of confidence using their skills. The sector now has a bank of baseline evidence which has contributed to a meaningful analysis of the anticipated skills gaps. Conclusions – This paper focuses on the critical importance of implementing evidence-based practice in public libraries. In an interactive workshop, managers determined the skills priorities at both the local and sectoral levels to inform staff development programs and recruitment activities. A collaborative SLV/PLVN project workgroup will implement the report’s recommendations with a state-wide workforce development plan rolled out during 2015-17. This plan will include a training matrix designed to bridge the skills gap, with a focus on evaluation strategies to monitor progress towards objectives. The paper provides insights into the different ways in which the project workgroup is using research evidence to drive practice.
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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 | 0.286 | 0.425 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.021 | 0.019 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".