Bibliographic record
Abstract
Since the publication of the Alberta Journal of Educational Research's (AJER) Teacher Education and Teaching Special Issue # 1 in Fall 2020, I report that in Spring 2021, the COVID-19 pandemic is still present and an enduring and grave global concern.Locally, in Alberta, the United Conservative Party government continues to put in place harmful policies, regulations, and laws that, as examples, reduce support for public education and health care, remove access to post-secondary education, dilute sustainable and fair labour, delist provincial parks, and discount clean environment enterprises.Nationally, as a tribute to the coast-to-coast implications for education and teaching that reside in this special issue, the events and happenings of this past year unequivocally remind us that the health and well-being of our citizens and land, all of them and every square inch, respectively, are reason enough to speak and act.One constant is the dedication of educational scholars, researchers, and teachers, as is evidenced in this AJER Teacher Education and Teaching Special Issue # 2. Similar to the first issue, the second theme issue explores topics designed to advance teacher education and teaching theory and practice.The first article in this special issue, written by Marilyn Abbott, Kent Lee, and Marian Rossiter, is titled: Research Mobilization in TESL Reading Groups: Benefits, Challenges, Supports, and Procedures.A key aspect in this article is the findings of a longitudinal study.These authors document how professional reading groups, comprised of 76 English as Second Language instructors in Western Canada, networked to promote reflective and viable programming for their adult learners.The second article, On the Uses of Keeping a Notebook: Autobiographical Narrative Inquiry and Marion Milner's A Life of One's Own, authored by Christie Schultz, delves into the reflectiveness of journaling.The act of keeping a notebook becomes an examination of experience.This is a worthy technique for creating research-based notes, a means for researchers to consider their positionality in a study, and for educators to contemplate their practice in and out of the classroom. Exploring the Impact of an Anti-Homophobia and Anti-Transphobia Program on a Teacher Education Program: LGBTQ+ Pre-Service Teachers Identify Benefits and Challengesis the third article in AJER's Teacher Education and Teaching Special Issue #2.The authors, Jennifer Mitton, Joanne Tompkins, and Laura-Lee Kearns, drawing on the group interview method in this multi-year qualitative study, investigate the responses of four pre-service LGBTQ+ students.These students were enrolled in an Eastern Canada Bachelor of Education degree program that overtly developed welcoming environments for LGBTQ+ individuals.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.063 | 0.025 |
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".