Toward a Multi-Level Knowledge Building Innovation Network
Bibliographic record
Abstract
Knowledge building requires collaborative bootstrapping, with participants at all levels of the education system part of a collective effort to go beyond information exchange to innovation-producing networks that demonstrate that education can operate as a knowledge creating enterprise. Organizational theories and research are increasingly focused on multilevel perspectives for creating actionable knowledge; the challenge is to take advantage of emergence to self-organize around solutions and new means. By “innovation networks” we mean networks that go beyond sharing and discussion to the actual creation of new knowledge and innovations. Self-organization and emergence surround us, all the time and at multiple levels, whether we are aware or not. However, self-organization around idea improvement is rare and requires engaging innovative capacity at all levels, a research-intensive enterprise surrounding innovations, and an open source engineering team committed to enabling new forms of interaction, media, and analytic tools. “Multi-level” envisions inclusion of students, teachers, administrators, researchers, engineers, and policy makers in a collaborative enterprise. This session takes the form of a design think tank to advance conceptual frameworks and means for new and more powerful environments to support a multi-level knowledge building innovation network.
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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.015 | 0.021 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".