Bibliographic record
Abstract
What does it mean to take actions of one’s own to learn? How do human beings create meaning for themselves and with others? How can learners’ active efforts to build knowledge be encouraged and supported? In this edited compilation, scholars from a diverse range of academic and professional backgrounds address these questions, grounded in the conviction that the ability to take effective action of one’s own to learn is itself an essential form of knowledge. In an era of dramatic social, environmental and political change, the need to access vast amounts of information to make decisions demands that learners become active agents in their own knowledge development. Educators are transforming ideas about their role(s) as they strive to provide guidance to help learners take the lead in their own learning. Learners are building new ideas about their capacities to gather and organize information while working with others. No longer simply consumers of information, they are beginning to see themselves as capable and effective researchers. Researchers are also expanding ideas about their knowledge-gathering work and identities. No longer simply reporters of information, researchers are seeing themselves as learners, as they engage in deeper, more collaborative ways with participants in their research. Chapter authors describe their dedicated, and often career long journeys to show the vital connections between knowledge, acting to learn, identity and being. To engage in this work means disrupting traditional ideas about how knowledge is most effectively acquired. This book will inspire researchers, educators and educational planners as they build the kinds of new participative structures needed to support individual and collective actions to learn.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".