Current Thinking From and About Home Economics/FCS and Artificial Intelligence (AI)
Bibliographic record
Abstract
This paper shares current within-field and peripheral thinking about home economics and family and consumer sciences (FCS) and artificial intelligence (AI). Home Economics 3.0 is discussed relative to Industry 4.0 and Education 4.0. The research question was “What are home economists and FCS practitioners saying about AI at this early juncture?” The profession is becoming enamoured with AI, but only 15 home economist-authored papers were found with virtually all published in the last two years. Most were commentaries or opinion pieces with nominal research papers, many early-online. There was an emergent consensus that AI can bring home economics/FCS concepts to life if the profession critically embraces world-changing technological and digitization advancements. Research is needed about (a) using AI to teach home economics/FCS, (b) using home economics/FCS to teach about AI and (c) what constitutes rebooted Home Economics 3.0 that can handle Industry 4.0 and use Education 4.0.
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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 | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.032 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".