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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".