Bibliographic record
Abstract
Cafeteria Training curriculum falls under the Home Economics umbrella in the British Columbia curriculum for secondary schools. Cafeteria Training in teaching kitchens prepares students for entry level positions in the food service industry because classrooms teach the concepts and skills of industry cooking. At present curriculum written by the Ministry of Education is outdated. The culinary industry has shifted towards environmentally sustainable practices, which include sustainably produced food items, waste management, energy efficiency, water conservation, and green cleaning agents. This paper outlines several specific points for inclusion of current industry practices into the curriculum Integrated Resource Package. This paper also includes a handbook for Cafeteria Training teachers, Home Economics teachers, and food service professionals who are looking to move towards more sustainable practices in their classrooms and kitchens. This handbook is designed so it can be removed from this larger paper and be distributed to teaching kitchens and Cafeteria Training instructors in B.C. so they may use it as a tool to move their schools towards more sustainable practices. I have included active website links, email addresses, phone numbers and company information, current as of April 2014, for Chef Instructors to use.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.037 | 0.011 |
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".