Knowledge, Truth, and Schooling for Social Change: Studying Environmental Education in Science Classrooms
Bibliographic record
Abstract
While recent research trends in science education have focussed the collective attention at utilizing the science curriculum as a means towards positive social change, such efforts have largely been predicated on understandings of the nature of knowledge and truth as socially constructed entities. Through this lens of social constructivism, knowledge is said to bear the signature of individuals and institutions in power, and therefore extant knowledge is considered to be the vehicle for further oppression of disadvantaged groups. There are at least two ways in which this argument is deeply flawed—social constructivism accords to itself epistemic positions it denies others, and an intellectually honest application of its principles leads to a position where there is no way to distinguish between better or worse positions on issues. In contrast, the principle of social realism takes a ‘middle path’, acknowledging the social reality of knowledge construction but disavowing the relativism of social constructivism. Through this epistemological foundation, implications arise for curriculum theory—how is it that we may discriminate forms of knowledge for in/ex-clusion into the school curriculum? In this study, I consider the curriculum changes in the Ontario elementary science anxd technology curriculum. I ask two key questions: (i) What are the effects of the curriculum revisions on the knowledge content of the science curriculum? and: (ii) What are the characteristics of science pedagogy in fulfilment of these curriculum changes? I develop instruments to analyze curriculum documentation, and classroom pedagogy. The major findings of this project include: (i) the curriculum revisions have added environmental knowledge expectations with varying degrees of disconnection from the scientific content knowledge; (ii) knowledge expectations removed to accommodate environmental expectations constituted important scientific principles; (iii) environmental pedagogy in science classrooms reflected the disconnection between science and environmental knowledge, most obviously in the upper grades where the degree of boundary maintenance between knowledge forms was strongest; (iv) this disconnection between environmental and scientific knowledge forms inhibited the cumulative modality of knowledge (re)production. A discussion of results and the general principles of the importance of knowledge concludes the project.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".