Dilemmas in Using Phenomenology to Investigate Elementary School Children Learning English as a Second Language
Bibliographic record
Abstract
This paper is intended for doctoral students and other researchers considering using phenomenology as a methodology to investigate the experiences of children learning English as a second language in an elementary classroom setting. I identify six dilemmas or puzzling challenges likely to arise if researchers adopt a phenomenological approach to conducting research. The six dilemmas fall under two categories: fundamental and situational. Fundamental dilemmas include descriptive versus interpretive; objective versus subjective; and participant voice versus researcher voice. The former focus is on a fundamental understanding of phenomenology as a research method while the latter include language and cultural challenges and limitations of the researchers. Situational dilemmas arise from the challenges an investigator may encounter in using an in-depth interview as a research tool with children from different cultural and language backgrounds. I present these dilemmas so that researchers can understand more readily the challenges they may face in exploring the lived experience of these children.Keywords: phenomenology; English Language Learners; lived experience
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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 | 0.485 | 0.379 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.017 | 0.115 |
| Scholarly communication | 0.025 | 0.038 |
| Open science | 0.007 | 0.022 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.001 | 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".