Bibliographic record
Abstract
In Houghton’s (1989) review of educational paradigms, he highlights the gaining importance of chaos theory. Chaos theory is often characterized by the term non-linear. Chaos theory can be found in many disciplines; in structural engineering, the behaviour of a structure under earthquake loads is often seen in terms of non-linear behaviour. Another characteristic of chaos theory is unpredictability. The implications for educational theory, as Houghton suggests, is that we have a realistic model for what happens in highly interactive systems. If the process of teaching and learning is seen as a highly interactive environment, then the parallels to chaos theory can be easily seen. The nature of a lecture can change when a student asks a question. This results in a non-linear learning environment. Students affect how something is taught by their own unique ways of understanding. Houghton (1989) suggests that the use of computers in education is supported by chaos theory. He suggests that computers should play a significant and active role with learning. Chaos theory not only supports the concept of using computers in education, it suggests that with non-linear programming (e.g., hypertext), education can change from the traditional linear format to a non-linear methodology that is alive and vibrant.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.178 | 0.111 |
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".