Bibliographic record
Abstract
There are substantial similarities between deep learning and the processes by which knowledge advances in the disciplines. During the 1960s efforts to exploit these similarities gave rise to learning by discovery, guided discovery, inquiry learning, and Science: A Process Approach (American Association for the Advancement of Science, 1967). Since these initial reform efforts, scholars have learned a great deal about how knowledge advances. A mere listing of keywords suggests the significance and diversity of ideas that have come to prominence since the 1960s: Thomas Kuhn, Imre Lakatos, sociology of science, the “Science Wars,” social constructivism, schema theory, mental models, situated cognition, explanatory coherence, the “rhetorical turn,” communities of practice, memetics, connectionism, emergence, and self-organization. Educational approaches have changed in response to some of these developments; there is a greater emphasis on collaborative rather than individual inquiry, the tentative nature of empirical laws is more often noted, and argumentation has become an important part of some approaches. But the new “knowledge of knowledge” has much larger educational implications: Ours is a knowledge-creating civilization. A growing number of “knowledge societies” (Stehr, 1994), are joined in a deliberate effort to advance all the frontiers of knowledge. Sustained knowledge advancement is seen as essential for social progress of all kinds and for the solution of societal problems. From this standpoint the fundamental task of education is to enculturate youth into this knowledge-creating civilization and to help them find a place in it.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.106 | 0.052 |
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".