WRITING CO‐RESPONDENTS: Teacher‐educators reflect on‘orienting’ new students
Bibliographic record
Abstract
In this piece, we present 11 mesostics (poems) that represent our experiences as 'corespondents' to a curriculum initiative in our faculty of education.We have taken our own documentation in the form of e-mails, including all of our free-wheeling conversations as friends, colleagues and scholar/researchers and subjected them to a number of aleatory operations 3 [L.alea die, aleator dice-player].We have imposed 1 A version of this paper (entitled Working toward an ethos of social justice: An orientation initiative for pre-service teachers at the Mount) was presented at the Canadian Association for Teacher Education (CATE), a special interest group within the Canadian Society for the Study of Education (CSSE), May 30, 2006 at York University, Toronto, Canada.2 C.O.R.E.stands for Collaborating on Research in Education and consists of the following, named in alphabetical order: Susan Brigham, Michelle Forrest, Valda Leighteizer, and Susan Walsh.Each contributor's work is equally valued; no one person in the group
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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 | 0.011 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".