Commentary: Thoughts on Three Decades in Literacy Education: Why Don't We Ever Learn?
Bibliographic record
Abstract
Literacy teaching and learning and education more generally seem destined to be buffeted by periodic pendulum swings between more and less regulation of curriculum, assessment and teachers’ work. Reflecting from a position of seniority, I speculate in this commentary on how the trajectory of progressive and generative theories and practices in literacy education might have been altered if such swings had not been so pervasive over the past several decades. Drawing on insights that have guided my own thirty-plus years of work to advance critically reflective and progressive literacy education, I suggest that greater attention to some wise words from past decades might help today’s educators to resist the never-ending pull of the pendulum.
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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 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.042 | 0.068 |
| Insufficient payload (model declined to judge) | 0.006 | 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".