Are We Bridging the Research Practice Gap? (Editorial)
Bibliographic record
Abstract
One of the key aims of Evidence Based Libraryand Information Practice is to bridge the research practice gap and make the findings of LIS research more accessible to library and information practitioners. I’ve therefore been keenly following the UK RiLIEs project(Research in Librarianship – Impact Evaluation Study; http://lisresearch.org/rilies-project/),which has been looking at ways to increase the impact of library related research for practitioners. The project culminated in a resources briefing(http://lisresearch.org/2012/07/10/research-intopractice-lis-research-resources-briefing/) whichI attended, and was thrilled to hear the project team report that the journal was one of the most appreciated sources of LIS research forpractitioners. My self-congratulation was alittle short lived, however, when the next set of findings presented was a range of resources that practitioners had heard of, but had yet to use – and sure enough, EBLIP was among them. Furthermore, other findings of the project included practitioners reporting a need for accessible summaries of research evidence!! The project team concluded that there was no shortage of research resources available to practitioners, but the challenge was finding the best way to make them available and easily accessible. As an open access journal, therefore, we need to work harder on publicizing the work we do. I’ve thus taken on board the recommendation that “here lies an opportunity for those with responsibility for freely available open access repositories of LIS research materials to raise awareness of their resources amongst the practitioner communities” (Hall, 2012). It is really important that as a journal we do take this message on board, as we have begun to find that the Evidence Summaries in EBLIP do make a difference. Over the past year, supported by a grant from the Canadian Association of Research Libraries, and led by our Associate Editor for Evidence Summaries, Lorie Kloda, we have been conducting a research project into the impact of Evidence Summaries. The project will be written up in full and the results published elsewhere, but in brief we validated a tool to assess the impact of the summaries on practitioners, used the tool to survey a number of Evidence Summary readers, and followed some of these up with more in-depth interviews. Initial results are promising, and we have found that Evidence Summaries impact on knowledge, individual practice, and more widely in the workplace of Evidence Summary readers. Earlier in the summer, we presented the results at a range of national (Canada and UK) and international conferences (in Europe and the US). Hopefully, these presentations (e.g., http://www.slideshare.net/lkloda/kloda-mla-2012-impact), will begin to further increase the awareness of Evidence Summaries – and perhaps turn some of that awareness into action. This issue sees a slight change in the Evidence Summaries, as described in Lorie’s editorial at the beginning of the Evidence Summary section. So, if you haven’t read an Evidence Summary before – I challenge you to read one today – and see if it makes a difference to your practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.104 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.022 | 0.028 |
| Insufficient payload (model declined to judge) | 0.023 | 0.019 |
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".