Accessible Elements: Teaching Science Online and at a Distance
Bibliographic record
Abstract
as lending themselves to a constructivist pedagogy. The big question, of providing laboratory experiences, is the subject of a full section, in my opinion one of the core questions in this book, and then a final section deals with some issues of the logistics and infrastructure of program delivery. This book will, I hope, be read by everyone with an interest in education. This is not only for science educators or distance educators alone. Certainly one hopes that teachers of science in the classroom -most of whom are likely to be called on in the future to teach at a distance at least in blended learning conditions -as well as those who already do teach at a distance, and also the administrators and policy makers who have to allocate and manage the resources that are available for science education will study this book carefully and glean from it some of the valuable ideas it provides for the expansion and improvement of distance education in the sciences. Surely our students deserve better programs, the out-of-school population needs more opportunity of continuing education in the sciences, and society deserves and needs a better return on its education and training investment in the sciences than it has enjoyed until now.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.056 | 0.010 |
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".