Bibliographic record
Abstract
ABSTRACT: One of the core competencies in the IFT Education standards is for students to achieve competency in communications skills (that is, oral and written communication, listening, interviewing, and so on). According to the IFT guidelines, by the time students graduate, they should not only be able to search for and condense information but also be able to “communicate technical information to a non‐technical audience.” The Education Division of IFT sponsors an annual writing competition for undergraduate students to bring attention to and promote the development of communication skills. The short essays can be on any technical subject or latest development in the food science and technology field that may be important to the consumer. The article must be written in nontechnical language such that someone reading a local newspaper could understand it. Due date for submissions is typically the first week in June. More information on eligibility, rules, submission, and judging criteria will be posted on IFT's Education Division website. Monetary prizes are awarded to the authors of the top 3 papers, and the winning entry is published in the Journal of Food Science Education (JFSE) each year. JFSE is pleased to publish this year's winning entry submitted by Stephanie Chiu from the Univ. of British Columbia.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.008 |
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".