Educating for Identity: Problematizing and Deconstructing Our Literacy Pasts
Bibliographic record
Abstract
In order to become effective teachers of language and literacy, it is critical for teacher candidates to have a sense of who they are as literate beings, how their literacy pasts have been lived, and how this might have an influence on the students in their classrooms. As teacher educators, we should not allow teacher candidates to rest simply with the recollection of key literacy events and memories. In order to be fully aware and wide awake to the complex task of teaching language and literacy, teacher candidates need to be engaged in active discussion that involves problematizing and unpacking their experiences, memories, and stories and what they really mean in past and present conceptualizations of literacy and sociocultural contexts.Pour devenir des enseignants de langue et de littératie, il est critique que les stagiaires aient un sens d’eux-mêmes comme êtres lettrés, qu’ils soient conscients de leur passé en matière de littératie, et qu’ils aient une idée de l’influence de ces facteurs sur leurs élèves en salle de classe. En tant que formateurs d’enseignants, nous ne devrions pas permettre aux stagiaires de se limiter à des souvenirs portant sur des événements relatifs à la littératie. Afin d’être pleinement conscients et éveillés face à la tâche complexe qu’est celle d’enseigner la langue et la littératie, les stagiaires doivent prendre part à des discussions actives, problématisant et déballant leurs expériences, leurs souvenirs et leurs récits personnels, et analysant leur sens selon les conceptualisations du passé et du présent de la littératie et en fonction des contextes socioculturels.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.036 |
| Scholarly communication | 0.014 | 0.023 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".