Combining Photography and Duoethnography for Creating a Trioethnography Approach to Reflect Upon Educational Issues Amidst the COVID-19 Global Pandemic
Bibliographic record
Abstract
This study proposes a new research design, combining duoethnography and photography into a trioethnography that opens an artful lens through which educational changes, emerging and unfixed problems, and unexplored/unseen values and hopes were examined. Central to this trioethnography was voices of three Vietnamese doctoral students with transnational experiences in Canada and Australia, around three educational topics: researcher positionality, education inequity, and mindfulness in relation to the global crisis within and beyond higher education contexts. Living transnationally in these insecure and rocky times of a pandemic gives us a unique opportunity to contemplate the global educational shifts, moves, and changes in and after the crisis. We formed our discussions in the three circles of the Indigenous approach, in which we shared cultural artifacts, such as photographs, and used them as the catalysts for personal and interactive reflections. Taking up the spirit of duoethnography, we have seen that findings might be emerging from our dialogues and discussions; we gathered three different voices of contemplative educators to juxtapose our diverse perspectives and experiences of how education is changing, evolving, and shifting significantly amidst the COVID-19. In this process, we have shown efforts in progressing the traditional methodological practices of duoethnography through our trioethnographic conversations. Within the back-and-forth conversations, we have seen multiple facets of our narrative experiences through photographs, including personal sophisticated emotions, struggles, hopes, and losses.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
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 | 0.017 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.009 | 0.020 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".