How Educational Philosophies Shape Family and Consumer Sciences and Home Economics Education: A Commentary
Bibliographic record
Abstract
I began my teaching career in the mid-1970s totally unaware of the construct of educational philosophies. I attended teachers' college (1970–1972) to obtain a home economics teacher's license and certificate rather than a Bachelor of Education degree within an Education department. I do not remember ever being taught about this aspect of teaching. That is not to say it was not taught, just that I do not recall internalizing educational philosophy as an important part of being a home economics educator. It was not until 25 years later (early 2000s), when I began teaching home economics teacher education methods courses at a university (as part of an Education faculty, not a Home Economics department), that I discovered the notion of educational philosophies as espoused in educational foundations courses and textbooks. From then on, philosophical awareness was the mainstay of my approach to teaching preservice home economics teacher education methods in these courses.
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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 | 0.015 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.031 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.040 | 0.044 |
| 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".