Advocating for a more active role for the user in LIS participatory research: a scoping literature review
Bibliographic record
Abstract
Purpose Through a review of the literature, this article seeks to outline and understand the evolution and extent of user–participant involvement in the existing library and information science (LIS) research to identify gaps and existing research approaches that might inform further methodological development in participant-oriented and design-based LIS research. Design/methodology/approach A scoping literature review of LIS research, from the 1960s onward, was conducted, assessing the themes and trends in understanding the user/participant within the LIS field. It traces LIS research from its early focus on information and relevancy to the “user turn”, to the rise of participatory research, especially design-based, as well as the recent inclusion of Indigenous and decolonial methodologies. Findings The literature review indicates that despite the reported “user turn”, LIS research often does not include the user as an active and equal participant within research projects. Originality/value The findings from this review support the development of alternative design research methodologies in LIS that fully include and involve research participants as full partners – from planning through dissemination of results – and suggests avenues for continuing the development of such design-based research. To that end, it lays the foundations for the introduction of a novel methodology, Action Partnership Research Design (APRD).
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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 | 0.171 | 0.286 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.044 | 0.039 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.020 | 0.021 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.008 | 0.006 |
| 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".