Bibliographic record
Abstract
In the spring of 1995 Stephen Baker, a staff member at Goderich District Collegiate Institute within the Huron County Board of Education (now the Avon-Maitland District School Board in Ontario, Canada), took a board-sponsored course on the fundamentals of HTML. The natural fit of education and the Internet was immediately apparent. In the fall of 1996 Baker used Notepad to construct an 11th-grade biology course, which was posted via wsFTP onto an Odyssey Network Inc. server housed in the back room of a jewelry store in Clinton, Ontario, under the https://www.w3.org/1999/xlink" xlink:href=" https://HuronWeb.com ">HuronWeb.com domain ( https://www.w3.org/1999/xlink" xlink:href=" https://WHOis.com ">WHOis.com ,n.d.-a). The biology online course was viewable to anyone connected to the Internet. In the spring of 1996 Baker posted a second course, Canadian Literature, written by John Smallwood. This course won a North American award for course development ( Hall, 1997 ). On January 2, 1997, the fledgling school was moved to the https://www.w3.org/1999/xlink" xlink:href=" https://VirtualHighSchool.com ">VirtualHighSchool.com domain ( https://www.w3.org/1999/xlink" xlink:href=" https://WHOis.com ">WHOis.com , n.d.-b ). During these early years, Baker pieced together a rudimentary learning management system (LMS) to run the two online courses, providing a log-in so that students could gain access to their courses, a secure testing site, and a rudimentary role structure for students, teachers, and administrators.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.014 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.575 | 0.220 |
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".