Bibliographic record
Abstract
In 1972, having lived in Saskatoon, Saskatchewan for over a decade, my parents felt called to New Zealand – my father, Will, to undertake his doctorate in education at the University of Otago, my mother, Kate, to establish a tertiarylevel speech pathology training course in Christchurch. It was the second time they had been drawn to those islands. The first such occasion, in 1950, involved three £10 passages – for them and my brother William – and a long voyage from their home in the British Isles via the Atlantic Ocean, Caribbean Sea, and Panama Canal, and south through the Pacific Ocean to New Zealand’s North Island. There, they made their way to a small hamlet called Mokauiti, ‘as the crow flies’ about 89 kilometres (56 miles) from the Waikato regional capital of Hamilton. In that remote place, and for just over three years, Will was the sole teacher in a two-roomed school, and Kate minded both William and my sister Caroline, born (literally) at the school in 1952 – so remote the settlement and so muddy the roads that the doctor could not get through in time to deliver her. But in 1953, the family returned to the north of England, later adding sister Nicola to the mix, before realising that life in the UK remained parlous in the reconstruction period following World War II. So they headed off again, this time to the backblocks of rural Saskatchewan, and to the embrace of another hamlet, Porcupine Plain. The changing needs of pubescent children, and more varied opportunities to practise teaching and speech pathology then enticed them to Saskatoon, 280 kilometres (173 miles) away where, in time, I arrived.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.448 | 0.279 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".