Educator Identity Development on The Trans-cultural Journeys
Bibliographic record
Abstract
We are three emerging educators, from the East to the West, reflecting on our lived experiences in Asian educational contexts and shaping our identities through a connection between the motherlands and the places we immigrated to. Our educator identities have been grounded on the social inequities we experienced in Asian education through the lens of culturally and socially Asian teachers studying in Canadian institutions. We story our lived experiences by using photo-voice research method to elicit our East-to-West transcultural journeys. After elaborating on our stories, we have found that our identities enable us to shed light on the influences of the Three Teachings or Religions: Buddhism, Confucianism, and Taoism across Asia on teachers' mindset which causes inequities to the marginalized. Significantly, drawing on our experiences, we attempt to discuss how we reform the educator identities within us in a Canadian education context where equity, diversity, and inclusion are crucially acknowledged.
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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 | 0.015 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.044 | 0.023 |
| Scholarly communication | 0.020 | 0.011 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".