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Record W6996221404

Qualitative research as a driving force focused on reflection and the constant rethinking of what we do, and how and why we do it

2024· other· en· W6996221404 on OpenAlexaboutno aff

Bibliographic record

VenuePortuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) · 2024
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsQualitative researchConversationFeelingHumanityMeaning (existential)ClosenessGrounded theoryNarrativeData collection
DOInot available

Abstract

fetched live from OpenAlex

Editorial |EN The latest trends in qualitative research clamor to include practices that are ever more creative, more visual, more critical, more reflective, more collaborative, and even more multimodal. Rethinking and validating qualitative research as a more integral practice revives the original celebratory feeling related to qualitative research in its most intimate dimension: humanity. Why do we undertake qualitative research? Essentially, we engage in qualitative research to understand more and to understand better. In becoming closer to our humanity and the constructivist experiences that shape us on a daily basis, we forge ourselves as qualitative researchers in the constant search for the very meaning of the social phenomena relevant to our realities. Human, ever more human. Part of this human reality must bring with it significant doses of individual and collective reflection, of closeness to other latitudes, of constant criticality, of the systematic questioning as to the what, how and why of our qualitative research. We must investigate more on upcoming future trends so that they can form part of the conversation in the present. This conversation must include discussion about the effective and ethical use of communication and information technologies. It must question our data collection and analysis processes. Are we promoting free, safe, artistic, and symbolic spaces for our participants to explore their experiences and perceptions? Should we rethink the interview as an official mechanism for the collection of in-depth data? Could we think about other data collection techniques in which the participants can become the true protagonists of their own realities? Do we dare to rethink new trends that could guide participants in representing their realities more organically? As qualitative researchers, we must open ourselves to more participatory processes at community and regional levels; we must open the door to internationalization. We must, in turn, internalize conductive practices of reflective and critical questioning to challenge our own biases. Could the ethical and conscientious driving force in our work revolve around our self-awareness, our taking into account our own positioning in qualitative research studies? Could we celebrate open mindedness, embracing difference and embodying humility as fundamental pillars for those of us who dare to do qualitative research? Humble, ever more humble. Volume 20, Issue 1, of the journal New Trends in Qualitative Research, presents an enriching collection of these trends in qualitative research. Mohd Anis and Olisa (2024) present a pilot study that uses artificial intelligence in focus group processes as a mechanism to rethink data collection in a way that guarantees depth and scalability. Along similar lines, De Almeida et al. (2024) problematize pedagogical practices for teacher training and to guarantee the right to education in Brazil. Veras Meira and Fernandes (2024) reflect on decolonization and the need to institute new beliefs for a more emancipatory education. Picolo Gimenes et al. (2024) show how the use of the Drawing-Story Procedure promotes graphic expression through therapeutic play, and humanizes the collection of qualitative data, establishing ludic-affective links between children and nursing staff. Another study seeks to identify appropriate practices and strategies to minimize risks and impacts for patients who suffered from COVID-19 (Miranda et al., 2024). From Peru, an article reflects on the semantic and conceptual distinctions between theses, thesis reporting and scientific articles as academic practices in higher education, while another reflects on effective marketing strategies along with the challenges faced by some exporting companies in this nation. A research team from the University of Aveiro conducted a bibliometric and systematized study asking what is meant by plagiarism and self-plagiarism. In addition, a study carried out in Canada exemplifies creativity in qualitative research by using teaching innovation and research on primary school Social Science teachers. The authors Araújo-Oliveira et al. (2024) used socio-spatial analysis and the evaluation of musical works to promote professional development in teachers. Similarly, considerations rethinking the training of teachers in teacher-training colleges are presented, such that teachers can learn about the ontological, epistemological, and methodological dimensions of their educational research. Summers et al. (2024) make a reflective call for the collection of qualitative data on online contexts. Finally, presentation is made of an exploratory compilation of arts-based methods to assist in understanding the experiences of people working in care provision for the elderly. Techniques found in the compilation include the visual arts, photoelicitation and storytelling, among others. Such techniques deepened understanding of the emotional, psychological, and social perspectives of caregivers, while also promoting processes of self-reflection and dialogue in these. Such arts-based methods promise to position themselves as integral processes in qualitative research. The articles presented in this issue position qualitative research as a driving force focusing on reflection and the constant rethinking of the what, how and why of what we do. We hope that on reading this issue, you find more questions than you do answers, because we all owe ourselves this constant questioning, knowing that the conversation arising from qualitative research will always lead on to more. Reflective, ever more reflective.

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 imitation

Not 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.

metaresearch head score (Codex)0.064
metaresearch head score (Gemma)0.218
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.936
Threshold uncertainty score0.341

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0640.218
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0040.002
Science and technology studies0.0070.017
Scholarly communication0.0210.015
Open science0.0050.005
Research integrity0.0120.022
Insufficient payload (model declined to judge)0.0160.007

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.

Opus teacher head0.156
GPT teacher head0.475
Teacher spread0.318 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designTheoretical or conceptual
DomainMethods
GenreMethods

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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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