Networks of practice in science education research: A global context
Bibliographic record
Abstract
Abstract In this paper, we employ cultural sociology and Braj Kachru's model of World Englishes as theoretical and analytical tools for considering English as a form of capital necessary for widely disseminating research findings from local networks of practice to the greater science education research community. We present a brief analysis of recent authorship in top‐tier science journals to demonstrate the salience of English language dominance as an issue in our field and we share narrative reflections from 11 international science education researchers offering perspectives from the field about the challenges faced by researchers in local and global contexts. Using an interpretive research stance, we discuss these narrative reflections to illuminate the role of personal and collective responsibility of individuals, organizations and institutions within local social networks of practice to recognize the relationship between capital, power, and equitable participation within a global science education research community. We conclude by discussing some existing structures within local networks of practice that relegate some members of the community to peripheral participatory roles in the global community and we suggest new structures to support individuals to more equitably contribute to the production of knowledge in the field of science education in ways that benefit not only individuals, but also the global science education community. © 2011 Wiley Periodicals, Inc., Inc. J Res Sci Teach 48: 592–623, 2011
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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 | 0.025 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.052 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".