Rhizo-Autoethnographic Research: critical theories and the understanding of research methods/methodologies in qualitative studies
Bibliographic record
Abstract
Critical theories continue to reshape society in various ways and to enact changes in the education field. Scholars have generated ideas about classroom management, learning and teaching practices, and the curriculum itself, adding to each of these extensions an accurate and situated view of what needs to be worked on. The concept of contextualization has expanded views towards different positions taken by all those who have been involved in and affected by educational institutions and their far-reaching impact on education at all levels from pre-school to post-graduate and adult learning. With the amount of studies deriving from such practices, researchers have dealt with encounters in different places with people from diverse backgrounds. As much as it is an enriching opportunity, the sheer diversity challenges researchers to think of how they would necessarily apply methods and methodologies in the development and analysis of such complex experiences. Through rhizome and autoethnography perspectives, findings suggest that broadening understandings of doing qualitative research can yield not an easier approach for researching but rather a complex experiment in considering researcher positioning outside of conventional discourses.
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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 | 0.212 | 0.174 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.013 | 0.101 |
| Scholarly communication | 0.020 | 0.024 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier 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".