Bibliographic record
Abstract
If status of a school subject is at issue, then content, benchmarks, and standards cannot be underestimated. Of course, the question is what content and what (or whose) standards? Technology has suffered as a school subject in many ways because of the lack of consistent content and a defensible set of standards. What technology should a student in grade 2 know about and be able to use? What about grade 6, grade 8, grade 10, or grade 12, at graduation? What are the benchmarks for each grade level? We do not yet know. Should we have consistent technology content and standards for all students from K-12? Should all teachers abide by the content and standards? Should we have exams to monitor the students and teachers? Or should teachers have the freedom to teach what they want? If a student moves from one school to another, he or she will face a different curriculum with different goals. But the teachers will have the freedom and power to make professional judgments about what to teach. Who should make these judgments?Request access from your librarian to read this chapter's full text.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.008 |
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".