For Optimal Inclusivity in the Research Process, Researchers Should Reflect Early and Often on How to Create Welcoming Research Environments
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,428 | 0,592 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,005 |
| Méta-épidémiologie (sens large) | 0,008 | 0,003 |
| Bibliométrie | 0,008 | 0,009 |
| Études des sciences et des technologies | 0,025 | 0,038 |
| Communication savante | 0,043 | 0,056 |
| Science ouverte | 0,008 | 0,021 |
| Intégrité de la recherche | 0,016 | 0,035 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 0,012 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».