Towards an understanding of human and ecological flourishing
Bibliographic record
Abstract
Various schools of thought and critical theories have been brought to bear upon the field of social justice science education, which has begun to garner some much-needed attention (e.g. Calabrese Barton & Upadhyay, 2010; Emdin, 2011; Schindel Dimick, 2012). While each contribution to this nascent field may represent a step forward—that is, towards more socially just and egalitarian educational experiences and outcomes for science learners and their teachers—attention must also be placed on understanding how the varied directions of social justice in science education research work together. The field is fragmented. Some areas of social justice research flourish, such as attending to students’ opportunities to access science courses and scientific discourses and practices, while other aspects of social justice in science education remain under-researched, under-theorized, and under-critiqued. Such discontinuity may lead science educators to struggle to define the field and to feel puzzled by a lack of coherent direction in the field. In this essay, I explore the concept of flourishing in an attempt to illustrate how it might contribute to an understanding of social justice science education’s goals and purposes.
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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 | 0.009 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.121 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".