In Conversation with the Self and Others: The Ethical Dilemmas, Ethical Challenges, and Values of Elementary Language Arts Teachers in Ontario
Bibliographic record
Abstract
K–12 teachers are required to make numerous, challenging decisions in their daily work. Many of these decisions leave teachers with ethical dilemmas where they must make reasonable decisions between competing sets of principles in complex, value-laden, and uncertain environments (Ehrich et al., 2011; Levinson & Fay, 2016). Education reforms can significantly change teachers’ working conditions and exacerbate ethical dilemmas (Fransson & Grannäs, 2013). Given this context, this study explores the ethical implications of education policy in practice, focusing on the experiences of five Ontario grades 1–8 teachers working during a period of Language Arts (LA) education reform. Grounded in theoretical perspectives that position teaching as a moral activity (Hansen, 1998; Jackson et al., 1993), this study addresses three questions: what values do these teachers prioritize in their LA teaching? What ethical dilemmas and challenges do they experience in teaching grades 1–8 Language Arts and why do they experience them as such? How are these teachers impacted by their ethical dilemmas and challenges? Using a qualitative methodology, I conducted a semi-structured interview with each participant and five focus group discussions. Thematic analysis was performed using data from surveys, interviews, and five normative case study focus group discussions held over 10 weeks. Using the Brighouse et al. (2018) Educational Goods Framework as a conceptual lens to interpret the study’s findings, I unpack teachers’ ethical dilemmas and challenges related to institutional policies surrounding grading, assessment, and resource distribution in LA. While both teachers and the system seemed to share core values of achievement and accountability, a deeper examination revealed that their conflicts arose not from a disagreement over these values themselves, but from divergent meta-values—differences in how values were and should be interpreted and enacted in practice. Participants’ ethical dilemmas and challenges contributed to feelings of demoralization; yet, they also expressed a strong sense of moral responsibility. This study holds implications for research, policy, and practice, shedding light on the ethical implications of policy in practice. Ultimately, I call upon various education stakeholders to support teachers with the ethical dimensions of their practice and offer recommendations on how to do so.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.046 | 0.032 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".