Investigação Qualitativa e o Desafio Digital // Qualitative Research and the Digital Challenge
Bibliographic record
Abstract
Framework- In the context of the need for the production of knowledge in low- and middle-income countries, as well as in high-income countries with their socially vulnerable populations and the concomitant, minimal availability of funding for international research, university researchers should innovate. Goal- Discuss critical methodological issues in the process of designing and implementing international online survey research. This is done in the context of responding to the need for innovation in data collection tools to expand their responsiveness to the international field and the participants’ characteristics. Chapter organization- The chapter is organized with the presentation of online international research, first presenting insights for an alternative and innovative design, then formulating questions to remotely collect international data, renewing a dialogue setting, and exploring issues of recruitment, attrition and participation. It also reports successful experiences of the internationalization of research, intellectual partnerships and shared successes in the process of creating, exchanging and translating knowledge in the context of global health and the democratization of knowledge. The experiences are related to qualitative inspired research implemented in the continental sphere (Africa, South and North America, and Europe) with the creation of survey questionnaires for an exploration of narratives, experiences, and decisions. Final consideration- The mobilization of researchers’ social and professional networks, in addition to the constant reformulation of intellectual partnerships in research, are today the most common strategies to face the current challenges in academia. Innovation for methodological advances in audacious design for unpredictable fieldwork may require the revisiting of epistemological grounds. Emerging issues in this type of research, such as “research fatigue”, should be considered.
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 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 | 0.166 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.033 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".