Bibliographic record
Abstract
The thesis was based on a project commissioned by Lethbridge College. The aim of the project was to create a \nmanual for the teachers in Lethbridge College who wish to teach an electronic course for Estar, a Chinese \ncooperation college. \nLethbridge College had experienced a problem with providing information for teachers who wanted to teach the \nelectronic course. The teachers had not known from whom and where to ask their questions. Therefore, they \nasked their questions from the most visible member of the cooperation with China, the head of the China \nproject. \nThe project was scheduled for winter 2007. The work began with research and continued with meetings with the \ndifferent departments of t he school. The cooperation with China involves many of the departments of the \nschool and th eir expert opinion needed to be considered for the manual. The m anual was then compiled, \nformatted and copyright protected. After the manual was completed, a meeting introducing the manual was held \nin order to familiarize the manual to the teachers. The manual was analysed with a SWOT analysis and the \nrecommendations given were based on the analysis.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.179 | 0.046 |
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".