Virtual Special Issue on “Collaborative Practices in Mixed Methods Research”
Bibliographic record
Abstract
Mixed methods research (MMR) is inherently a boundary spanning practice (Archibald, 2016).Common to this understanding is that boundary spanning occurs over the methodological borderlines of qualitative and quantitative research approaches.Equally relevant is the boundary spanning that takes place within and between investigative teams, communities, stakeholder groups, and bodies of knowledge.Bridging across boundaries attends to the spirit of diversity characteristic of MMR (Greene, 2007), enables collective responses to complex social problems (Lucero et al., 2018;Poth, 2018), provides opportunities to approach persistent challenges from new angles (Johnson et al., 2007), and reflects broader academic and social movements emphasizing the merits of partnership in improving research capacity, quality, and relevance to endusers (Archibald et al., 2023).Collaboration is a boundary spanning practice that is integral to these objectives.Broadly speaking, collaboration refers to the act of working together with the aim of producing or creating.Yet, working together can take many forms.Within this Virtual Special Issue (VSI), collaboration includes collaborative strategies used at one stage of the research process, the dynamics and structure of collaborative academic teams, partnerships between academia and other sectors such as front line service providers, through to engaged partnerships with communities in the form of community-based participatory research (CBPR).Collaborative practices are therefore defined widely and considered as a continuum, reflecting discrete participatory methods, and the collaboration occurring within mixed methods research teams (e.g., multidisciplinary, interdisciplinary, and transdisciplinary), including deliberate approaches to team composition and team dynamics.Collaborative practices between researchers, community, and participants, as well as guiding frameworks promoting collaboration, are also reflected along this continuum.As Katz and Martin (1997) indicate, there is often an assumption that collaboration in research is well understood.I contend there are many different forms and purposes of collaboration that are employed by mixed methods researchers and that can inform numerous MMR practices.The 18 articles curated for this virtual special issue highlight a range of such approaches, recognizing that equal partnership is not always attainable-or even desirable-depending on the nature of the research and questions of interest.These articles reflect a collaborative continuum categorized according to thematic heading, including (1) discrete participatory methods, (2) within and
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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 | 0.017 | 0.051 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.159 | 0.047 |
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".