Bibliographic record
Abstract
Departures The two-level suburban train clanks out of Central Station. Heading away from the city, it's not as crowded as the incoming trains, but I still find myself having to stand by the doors. We creak and rattle across several sets of points before finding our suburban heading, passing through increasingly unfamiliar station names. Outside, it's an achingly clear blue morning; the air is chilly, but the sun is starting to warm the day. It's June, which means the start of winter and the end of the semester just round the corner. Essay marking and clear blue skies. And that end-of-semester exhaustion. It's been another long semester: conferences in Manila, Vancouver, Singapore, Abu Dhabi; I was teaching from 7 to 9 last night; and here I amon a Friday morning, heading off to find a small language school somewhere in the suburbs whose address I fortunately remembered to print off from my e-mail late last night. The TESOL (teaching English to speakers of other languages) practicum. For many of us involved in teacher education, the teaching practicum holds, I think, a certain ambivalence: It's hard work; it's disruptive; it involves lots of traveling; it's too time consuming; it demands that we show expertise in a domain from which we are often increasingly distanced in our current work.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.019 | 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".