A Dialogic Construction of Ethical Standards for the Teaching Profession
Bibliographic record
Abstract
Ethical practice lies at the core of the teaching profession. The establishment of an agreed upon set of ethical principles by both the teaching profession and the public provides a collective understanding and vision for the professional judgment and action of educators. Reflective analysis of ethical practice and the use of multiple inquiry processes were used to review and construct an ethical framework for the teaching profession in Ontario, Canada. Both the educational community and the public were involved in this construction of ethical standards. The consultative and dialogic inquiry processes that were developed invited feedback, participation and leadership from students, the teaching profession and the public. These groups worked together to create and to validate the ethical standards that guide the individual and collective professional practices of Ontario educators. A dialogic construction requires that participants engage in communication that is relational, dynamic and builds on new insights that emerge from the shared understanding and knowledge of these individuals (Bakhtin, 1981). This paper describes both the process and the outcomes of this dialogic experience.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.032 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.023 | 0.099 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.007 |
| 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".