Bibliographic record
Abstract
ABSTRACT: The current IFT Education Standards used to evaluate Food Science programs for IFT approval have been in place now for 5 years. Most Food Science programs in the United States (as well as some in Mexico and Canada) have been reviewed according to these standards. The transition to instruction based on assessment of student learning outcomes, in accord with these Education Standards, is well under way. In the first round of reviews, the Committee on Higher Education (CoHE) focused mostly on the Core Competency grid, also making sure programs were writing learning outcomes and instituting meaningful assessment programs. In the 2nd round of reviews, there will be more focus on assessment of learning outcomes. CoHE would like to see that programs (administration, faculty, and students) have embraced the transition to assessment of learning outcomes and are making significant progress in aligning the curriculum with this educational format. As in the past, CoHE offers assistance to any program that would like help in making this transition. Finally, the next revision of the IFT Education Standards is due out in 2011 (10 year cycle) and it is already time to think about what this might entail. Some thoughts and suggestions for future directions are provided.
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 | 0.030 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".