Discourses of Conflict: A Multidisciplinary Study of Professional Education
Bibliographic record
Abstract
Thus we ought not to ask of a social institution: "What end or purpose does it serve?"but rather: "Of what conflicts is it the scene?"That is the way in which we shall come to an understanding of its mode of operation.(Passmore, 1964, p. xxii) "This is extremely promising work!What a wonderful teacher she will be!"During the first year of her teacher preparation program, Ping was acclaimed for an extraordinary term paper exploring Beethoven and the pedagogy of mathematics in the elementary school.Her mentors' anticipation was shortlived, however.During the final field experience in the second year of the program, a heated and anxious debate about Ping's English-language proficiency emerged among teachers, school principal, faculty, and the student herself.Ping withdrew from the program and the profession of teaching.How do we begin to understand scenarios like this?How is difference played out?How is conflict experienced, understood, negotiated, and contested?What do these understandings tell us about what counts in professional education and the profession itself?Are our understandings of conflict specific to each profession, or do the professions share frameworks for understanding?These are the questions that preoccupy this team of researchers, representing the four helping professions of education, medicine, nursing, and social work.These are the questions that are poorly understood as evidenced by the absence of a substantial literature on the topic.As a result, we lack knowledge and
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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 | 0.040 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.063 | 0.044 |
| Scholarly communication | 0.031 | 0.021 |
| Open science | 0.005 | 0.025 |
| Research integrity | 0.011 | 0.018 |
| Insufficient payload (model declined to judge) | 0.005 | 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".