Bibliographic record
Abstract
Since the publication of numerous texts regarding technology in education and 21st century learning, schools, districts and departments of education have been talking about the new learning for the 21st century. Included in this are emphases on critical thinking skills, problem solving, and the use of technology. This short discussion will focus on the latter of the three. The 2009 publication 21st Century Skills: Learning for Life in our Times could be described as helping to create a tipping point at least in the advance of the discussion of said technological skills. The text was embraced by a number of educational jurisdictions as a seminal work prescriptive of where technology will be taking us. I believe the question should not be where is technology taking us but where are we taking technology? I am skeptical; at the same time, I am not a Luddite. I must admit that there are a number of benefits to the utilization of technology, but the most evident one for educators is not, in my opinion, a momentous one. It is simply a potential increase in student engagement. To paraphrase a statement from Marshall McLuhan’s Understanding Media (1964), children are now born with square eyeballs
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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.055 |
| Scholarly communication | 0.022 | 0.033 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.006 | 0.008 |
| 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".