Bibliographic record
Abstract
I was fortunate enough to spend almost all of my working years in the field of adult education.Having been trained as an historian and having more by good luck than by clever planning found myself in a series of stimulating settings in that field, I frequently had an urge to write about adult education, its nature and its history, and about some of the issues involved in its development.After completing the course work on my master's degree in history (and completing a teacher training year) at the University of British Columbia, I went to work in Ottawa briefly for the National Research Council.I then moved back to UBC, joining the staff of the Extension Department, where I worked from 1954 to 1974, with an interlude of almost three years as assistant to the President of the University.I became Director of the Extension Department in 1967 and was in that position for seven years.In 1974,1 transferred to a faculty teaching position
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 | 0.050 | 0.190 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.025 | 0.023 |
| Scholarly communication | 0.030 | 0.017 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.016 | 0.042 |
| Insufficient payload (model declined to judge) | 0.025 | 0.009 |
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".