Identity text: an educational intervention to foster cultural interaction
Bibliographic record
Abstract
BACKGROUND: Sociocultural theories state that learning results from people participating in contexts where social interaction is facilitated. There is a need to create such facilitated pedagogical spaces where participants can share their ways of knowing and doing. The aim of this exploratory study was to introduce pedagogical space for sociocultural interaction using 'Identity Text'. METHODS: Identity Texts are sociocultural artifacts produced by participants, which can be written, spoken, visual, musical, or multimodal. In 2013, participants of an international medical education fellowship program were asked to create their own Identity Texts to promote discussion about participants' cultural backgrounds. Thematic analysis was used to make the analysis relevant to studying the pedagogical utility of the intervention. RESULT: The Identity Text intervention created two spaces: a 'reflective space', which helped participants reflect on sensitive topics such as institutional environments, roles in interdisciplinary teams, and gender discrimination, and a 'narrative space', which allowed participants to tell powerful stories that provided cultural insights and challenged cultural hegemony; they described the conscious and subconscious transformation in identity that evolved secondary to struggles with local power dynamics and social demands involving the impact of family, peers, and country of origin. CONCLUSION: While the impact of providing pedagogical space using Identity Text on cognitive engagement and enhanced learning requires further research, the findings of this study suggest that it is a useful pedagogical strategy to support cross-cultural education.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".