Bibliographic record
Abstract
The preparation of this issue of English Teaching: Practice & Critique has offered me the opportunity to reflect on how the body has figured in my work and study, first as a teacher and later a researcher. Memory is as much a bodily re-experiencing of sense and feeling as it is a mental process. As I recollect back to my pre-service induction into primary English language arts teaching in the early 1970s, I recall that my training afforded no appreciation of the bodily nature of reading and writing or teaching for that matter. My classmates and I were apprenticed in the received wisdom and professional lore on how best to teach such things as letter recognition and phonics. I remember the feel of the bright winter’s, Nova Scotia sun streaming through the wall of windows warming our prefab, barrack-like classroom, still in use some 25 years after Dalhousie University hastily constructed it to house the influx of post-war veterans. I remember the authoritative look and voice of the Sister of Charity who taught us the ins and outs of the current basal series employed in the province’s primary schools.
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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.047 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.008 |
| 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".