Bibliographic record
Abstract
Technology changes in the past two decades have changed computing power and mobility. Processing power has increased information-processing capability. Human interface technology has advanced with graphics evolution. Throughput has increased. Wireless/mobile technology has freed information flow geographically. Simulations enable us to witness phenomena at our own scale (nano to macro translated to real time). The result is more information available for increased amounts of time and with far greater spatial distribution. How do we know if the net increase in information leads to increased knowledge transfer and enhanced learning? There is a growing recognition that an enriched information environment is only part of the solution. We must proactively engage the mind of learners to receive, to process, to analyze, to synthesize, and to eventually generate new knowledge. Many innovations in educational pedagogy have the learner commit to the process of education under various names, including active learning, collaborative learning, or process education. How do we know if our innovations in pedagogy or technology contribute to learning? We will discuss the recent trends and innovations in educational pedagogy and technology environments and examine some aspects of what we know about how do we know.
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 | 0.016 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.024 |
| Scholarly communication | 0.013 | 0.029 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.024 | 0.012 |
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".