Bibliographic record
Abstract
Constructivism is common in all realms of science education parlance, including science education research, science teacher education, and science teaching. Though constructivism presupposes that students are not “empty vessels” to be filled, the language about educating students overwhelmingly reflects this notion. According to conceptual metaphor theory, sentences like, “I will deliver a lecture,” “she soaked up knowledge like a sponge,” and “they lack the knowledge necessary,” consist of metaphors that bely an understanding of learning as transactional; ideas are objects, the mind is a bounded container, and teaching and learning is a transaction. These metaphors subconsciously impact how we understand and approach teaching and learning. Having a transactional understanding of teaching and learning makes it difficult to understand, develop, and implement truly constructivist pedagogies. It also results in perceiving students using a knowledge deficit framework. It is easy, then, to blame students for not knowing what was taught. The knowledge was given, put out there, delivered, etc., but they did not “get it.” Awareness of this disconnection between speech, understanding, and actions, as well as knowing how the mind works, is important for creating more effective teaching and learning. Reflecting on the metaphors we use for teaching and learning can help us avoid perceiving students within a deficit framework. This shift in perception helps us to ask more useful questions as education researchers and fundamentally changes our responsibilities as instructors.
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 | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.064 |
| Scholarly communication | 0.021 | 0.039 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".