Reframing research on informal teaching and learning in science: Comments and commentary at the heart of a new vision for the field
Bibliographic record
Abstract
Abstract Informal science education is a broad field of research marked by fuzzy boundaries, tensions, and muddles among many disciplines, making for an unclear future trajectory (or trajectories) for the field of study. In this commentary, I unpack some of the hidden dimensions, tensions and challenges the five articles raise or point to implicitly in terms of theory, methodology, and future research. I explore ideas to think with in terms of learning pathways or trajectories and time‐space dimensions of science learning. I also explore future dimensions for partnerships, collaborations, boundary encounters and boundary objects. I conclude by raising issues pertaining to diversity, equity and the position of the research and researcher. Together, I call for attention to the subtle dimensions of ISE learning and development. I make the case for the legitimacy of yet marginalized theories in science education grounded in sociocultural theory and CHAT, social practice theory, and network theory. Most important, together with the authors, I make the case for a relational perspective of learning, identity and affect, as culturally and historically grounded. I suggest that these theories can be used to work through conceptions of partnerships that will help erase boundaries among cultures, practices, teaching and learning, constitutive of life‐long, life‐wide, and life‐deep science learning, science teaching and science education, and that in the end, will be transformative. © 2014 Wiley Periodicals, Inc. J Res Sci Teach 51: 395–406, 2014
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 | 0.060 | 0.239 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.020 | 0.036 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.009 | 0.012 |
| Research integrity | 0.041 | 0.067 |
| Insufficient payload (model declined to judge) | 0.003 | 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".