Extant Corpus on Intentional Learning Skill and Reflective Learning Log
Bibliographic record
Abstract
Intentional learning consists of the ability to learn how to learn, develop critical self-awareness and exercise full accountability for learning. It is highly effective in producing effective learners in a loosely structured learning environment in line with today’s student-centric teaching-learning paradigm. Intentional learning equips students with the necessary skills to actively participate in, self-direct, and regulate their learning so they can fulfill their goals. Through this metamorphosis, students develop intrinsic motivation and self-efficacy for learning, laying the groundwork for lifelong learning capability. The foundational framework of intentional learning is: (1) Learner’s trust and confidence in their learning capacity; (2) Learner highly engage in one learning and possessed critical awareness of what and how to learn; (3) Learner begins with the learning outcome in mind, realize the efforts required to unlearn, learn and unlearn with high valence; (4) Leaner able to master the learning content and learning objectives; and (5) Leaner able to exercise self-regulation and accountability in learning.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.030 | 0.008 |
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".