Teaching and Learning Research Literacies in Graduate Adult Education: Appreciative Inquiry into Practitioners' Ways of Writing
Bibliographic record
Abstract
Graduate students in Canadian universities who conduct research with human subjects as part of the requirements for their degree must submit a research proposal to the University Research Ethics Board and receive approval on the basis of compliance with the Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans (1998). This reflexive account of teaching and learning research literacies is based on a participatory research activity that the author has used during graduate students' introduction to a research-based, self-directed graduate program in adult education delivered at a distance. For the purposes of this paper, "research literacies" refers to any research practices that culminate in the writing of a research thesis, taking into account the procedures for compliance with the Tri-Council Policy. The focus of the reflexive account is an experiential classroom innovation with multiple cohorts of graduate students (8-12 students each) in which the faculty advisor as the principal investigator involves the graduate students as research participants in appreciative inquiry into practitioners' ways of writing. This participatory research into practitioner and researcher literacies offers some implications for teaching and learning the ethics of representation throughout the research process up to and including publication.
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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 | 0.061 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.021 | 0.073 |
| Scholarly communication | 0.027 | 0.013 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.004 | 0.008 |
| 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".