The power of reflective transformations
Bibliographic record
Abstract
The purpose of this paper is to analyze two students as partners (SaP) collaboration experiences and identify transformations of four undergraduate students and faculty who partnered to enhance an English as a second language (ESL) course taught at a Sino-foreign university. This paper utilized qualitative exploratory research with a pre-post design, where photovoice as a visual research method was adapted and used to portray and highlight the participants’ actual experiences and transformations that happened during the student-faculty partnership. The three transformations experienced by members of the partnership include: (a) the transformation from solitary to cooperative, referring to the closer relationship amongst faculty, student partners, and enrolled students; (b) the transformation from prey to predators, signifying the increased professional and learning capacity of both faculty and students; and (c) the transformation from inexperienced to mature, indicating self-growth based on overcoming obstacles. The findings have practical implications for future studies in that researchers can use the photovoice methodology to track participants’ experiences, except for traditional verbal and written data collection methods. However, the size of the research sample, which consisted of five females, limits the findings, and the uncertainty of the long-term effect of the transformations mentioned above should be further explored.
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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 | 0.071 | 0.154 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.008 | 0.077 |
| Scholarly communication | 0.015 | 0.022 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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, 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".