Bibliographic record
Abstract
Dr. Jane Elizabeth Brindley holds a Master of Arts in Counselling Psychology, University of British Columbia, and a PhD in Clinical Psychology, University of Ottawa. In 1978, she joined Athabasca University as Counsellor, Student Services. She went on to hold positions at the University of Ottawa, Northeastern Ontario Regional Cancer Centre; Contact North; University of Windsor; Oldenburg University; and University of Maryland University College. She currently works for the University of British Columbia. Brindley has played an active role in the Canadian Association for Distance Education (CADE), the International Council for Distance Education (ICDE), and the Women’s International Network (WIN). Her main research interests are learner support services in online learning and the tools to enhance this support. This chapter begins with photo and quote from Jane Brindley. It is followed by a brief overview by the authors on her activities, accomplishments, and contributions to online learning. A succinct comparative analysis between an interview with Dr. Brindley and the collective summary of all pioneers’ interviews follows. Next, a written transcription and an audio recording of Dr. Brindley’s interview about her experiences, perceptions, motivations, and research/leadership interests are provided. Finally, a list of publications provided by Dr. Brindley is offered.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.051 | 0.021 |
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".