Bibliographic record
Abstract
Who should design the curriculum that technology educators teach? Should curriculum be developed by governments and ministries of education? Should curriculum design be privatized and limited to commercial vendors? Should teachers design their own curriculum? Who should design the instructional materials? Should all materials be professionally designed by a vendor? As we noted in the previous chapter, technology teachers have had a century of freedom in designing and customizing their curriculum and instruction to suit themselves, their community, or the students. This had its advantages in diversity. The disadvantages, as we noted, related to the inconsistencies from school to school, even in the same district. When the teacher departed from a school, he or she typically departed with the curriculum and instructional materials. New teachers often began their first school year with little more than what they carried with them from their teacher preparation programs and student teaching experiences. One major problem was that when it came time for governments to identify priorities in the schools, technology studies was overlooked because of its incoherent curriculum. As indicated in Chapter VIII, the international trend is quickly shifting toward standards and unified curriculum in design and technology—the trend is toward a consistent scope and sequence of content for the study of technology. Common curriculum and goals along with content and performance standards are the trends. From a perspective of professional vitality and political finesse, these trends are healthy. These trends offer the potential for long-term sustainability of technology studies in the schools. Nevertheless, given that all curricula are fallible and have shortcomings, teachers will always have a need for dispositions toward, or skills and knowledge in, curriculum and instructional design.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.155 | 0.050 |
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".