Bibliographic record
Abstract
year olds finally meeting their online mentor in person. A shy voice exclaims ‘we thought you were a teenager!’ I tell them there is a seven in my age too, but that it’s followed by an eight. Gasp! Then an onslaught of voices compares my longevity with that of their grandparents, even greats – which I am also. Now I’m in. They have given me that status I had before, when we were all invisible. But then they would write “You ROCK” when they liked what I was telling them about their writing. This measures the potency – some call it magic – of WIER’s one-on-one contact between author mentors who spend 12-week terms mentoring creative work – poems and stories – and the students who send it to them online through participating schools. (You’ll find all of us on your computer when you go to www.wier.ca and follow all the links.) Trevor Owen, a Toronto high school teacher, was the inspiration for Writers in Electronic Residence, which he began in the 1980s. Its immediate success at this senior level prompted him to expand it to include students from kindergarten up. He asked me to assist in developing an elementary/intermediate program, since I had been giving language and music workshops in Scarborough public schools. These workshops had led to an O.I.S.E. experiment with a few authors responding to students’ submissions online. Trevor wanted this online response to continue, even though we agreed that working with younger children would lean heavily on motivation, as in “Keep Writing!” Critique would
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.118 | 0.059 |
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".