For Optimal Inclusivity in the Research Process, Researchers Should Reflect Early and Often on How to Create Welcoming Research Environments
Bibliographic record
Abstract
A Review of: Muir, R., & Coe, M. (2023). ‘Out of sight, but not out of mind’: A collaborative reflective case study on including participants with invisible disabilities in LIS research. Journal of Australian Library and Information Association, 72(1), 26–45. https://doi.org/10.1080/24750158.2023.2168115 Objective – To reflect on what it means to include people with invisible disabilities as research participants in research projects. Design – Collaborative, reflective case study using interviews. Setting – Doctoral-granting institution in Australia. Subjects – 2 LIS professionals who were also pursuing doctorates (practitioner-researchers) interviewed each other, each participant fulfilling the role of both interviewer and interviewee. Methods – The researchers did a reflective case study, each reflecting on their own past experiences of including people with invisible disabilities (PwID) as research participants in projects for their doctoral theses. They then interviewed each other and engaged in collaborative discussions. Each interviewer audio recorded and transcribed their own interview, which they also coded individually. The researchers then reviewed the individual coding together and subsequently created a single collaborative codebook that described the emerging themes. The researchers used NVivo software in the development of both the initial codes and final codebook. Main Results – The authors discuss four broad themes that emerged from their coding: “ethical approval for research,” “creating welcoming research environments,” “disclosure of invisible disabilities,” and “use of data.” Key topics in the discussion include questioning assumptions about research subject vulnerability, the value of being sensitive to individual participant voices, the difference between formal disclosure of invisible disabilities (ID) and disclosure that emerges organically throughout the course of an interview, and how research designs that do not consider PwID can create limitations on the use of data from PwID. Conclusion – The article authors noted that researchers should expect that those who participate in their research studies may be PwID, whether or not it is disclosed or explicitly relevant to the project. Thus, they suggest that when researchers shape the research design of their projects, they should thoughtfully engage in questioning their own values regarding inclusivity and not rely exclusively on ethics boards to support ethical and welcoming research environments. Thoughtful engagement might include researching what is involved in creating a safe space by considering such elements as lighting, seating arrangements, colors, and accessibility to restrooms and parking areas. In addition, the authors suggest that researchers should ensure flexibility and responsiveness within the research design and approach the project with full awareness of the impact ID may have on the research processes and the data. They indicate that researchers should remain open to acknowledging their own knowledge gaps, as well as educating others when opportunities arise. Additionally, they suggest that creating welcoming environments for research participants with ID is best done from the very beginning of a project, when it can be integral to the study design and should remain present throughout the course of the research process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.071 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".