Living in Stories Through Images and Metaphors: Recognizing Unity in Diversity
Bibliographic record
Abstract
ABSTRACT. Who are we as teachers and what constitutes a desirable educational experience? Two teachers, one Chinese and the other a white Canadian, tell “a single story [of teaching], integrated by our sense of ourselves᾿ (Crites, 1971, p. 303). Our storytelling is enabled by metaphors and images that serve as tools for reflecting on our actions in life and our teaching practice, and as a catalyst for understanding our teacher knowledge (Connelly & Clandinin, 1988; Hunt, 1987). VIVRE DANS DES HISTOIRES PAR LE BIAIS D’IMAGES ET DES METAPHORES : RECONNAITRE L’UNITE DANS LA DIVERSITE RESUME. Qui sommes-nous en tant qu’enseignants et qu’est-ce qu’une experience educative desirable? Deux enseignantes, une chinoise et l’autre une canadienne blanche, racontent “une simple histoire d’enseignement, integre dans notre perception de nous meme᾿ ( Crites, 1971, p.303). Notre recit d’histoire est rendue possible avec des metaphores et des images qui nous servent d’outils dans nos actions dans la vie et dans nos enseignements pratiques, et sont un catalyseur pour comprendre nos connaissances d’enseignant. ( Connelly et Clandinin 1988, Hunt, 1987).
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".