Bibliographic record
Abstract
Consider the hidden complexity of an experience that travelers frequently encounter: recently, while in Portugal, I walked past a car dealership and noticed a counter with the word recepção.Given the context, I concluded that it referred to reception desk (the similar pattern to the English word assisted in forming my opinion).However, when encountering the same word in spoken form, I was unable to recognize it-context and pattern similarity were lacking.This situation offers a vague glimpse into the foundational disconnect in education and research today: through curriculum design and adherence to particular research models, we essentially foster an outcome based on hidden assumptions.Learning, in contrast, is rich, multi-faceted, with each modality and medium resulting in different levels of understanding.Context, learner knowledge, medium of learning, and skills of the educator all contribute to formation of a learner's understanding.The common language of learning design and teaching reveal a bias held by many educators of learning as an act that can be controlled and managed toward an intended outcome.Recent criticism of minimally-guided instruction (Kirschner et al., 2006) and support of lecture formats (Burgan, 2006) set a tone of conflicting viewpoints to current ruling ideologies of "learner-centered" education.Unfortunately, these concepts are cast as opposed, when they ought to instead represent a gradient approach for use in quality teaching, learning, and research.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.192 | 0.038 |
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".