Smart technology for self-organizing processes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Learning technology periodically undergoes changes in response to changes in the prevailing models of human cognition and learning. A major shift throughout the behavioral sciences that began in the 1980s is beginning to have effects at the level of classroom learning and its supportive technologies. Inspired by complexity theory, it is a shift that treats all learning and knowledge building as essentially self-organizing processes. The design challenge is not to control the self-organizing process, as some instructional approaches attempt to do, but to facilitate the emergence of higher-level outcomes—e.g., better explanations, more coherent understanding. To foster such higher-level emergents, smart technologies not only need to support people interacting productively with other people but also ideas interacting productively with ideas and feedback systems promoting engagement between people and ideas. This is in contrast to conceptions of smart technology that see it as providing increasingly precise centralized control over learning processes. Smart technology attuned to the emergent character of learning and thinking does not simply turn more control over to the learners but shifts the emphasis from control to productive interaction among learners, teachers, ideas, and technology.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it