Bibliographic record
Abstract
Why should we teach technology in the schools? What is the reason for accommodating technology in the school curriculum? Why should we have to justify existence? Are the public schools an appropriate institution for developing economic human resources? Should students be taught to think critically about technology? Is the technology laboratory or workshop the place in the school where the students can “put it all together”? Will technology studies lose its identity in an alignment with math and science? Should technology studies serve to remedy long-standing inequities in technology? Should technology studies be aligned with ecology and sustainability? Is the future engineering education? These are some of the primary questions that impinge on the direction of technology studies in the schools. Throughout the 20th century, technology studies expended an inordinate amount of energy justifying itself. At times, it seemed as though this subject was trying to be all things to all people. In this chapter, we make the case that there is one, and only one, persuasive justification for the inclusion of technology studies in the schools. That justification is the content of technology. No one will buy all the things to all interest groups’ justification anymore.
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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.026 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".