Understanding my educational experience in rural China: a photo-elicitation self-study
Bibliographic record
Abstract
In this study, I will present the photos of my hometown in rural China to explore my six-year educational experience when I studied in an elementary school in the town. I took all the photos when I revisited my hometown this year. Through the photographs of different places in the small town, I intend to have a better understanding of myself and Chinese rural education. Under the theoretical framework of currere (Pinar, 2012), I will go through the four steps: the regressive, the progressive, the analytical, and the synthetic to explore my educational experience of the past, present and future. Moreover, I will use visual methods (Mitchell, 2011) to elicit my memories and to analyze the photos. With my research journals, my reflections and the close reading (Mitchell, 2011) of each photo, this research will provide a detailed account of my rural educational experience. After this self-study, I find rural experience shaped and keeps shaping my present self-knowing and future decisions as a teacher. I hope one day the photo-elicitation program can be applied in Chinese rural schools to facilitate rural teachers’ self-understanding and to improve the quality of Chinese rural 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".