Exploring Relational Politics in Social Learning: Dilemmas of standing too close to the fire
Bibliographic record
Abstract
… a common theme amongst critics (of the dominant educational paradigm) is that problems with human- environment relationships (out there) are intimately linked to ‘inner’ problems, that is, our collective perception and thought processes are impeded by a lack of awareness of our ‘in here’ condition. (adapted from Sterling, 2003:118) Sterling’s (2003) concern was that, because paradigms/worldviews/cultures/discourses function as ideologies that legitimise/justify courses of action, we need to learn how each of us is complicit politically in constructing subtexts by which our actions are judged to be reasonable. Such learning is not neutral and is contingent on processes of participation that engage people in thoughtful social action. One could say, as many environmental educators have for several decades, that at the core of environmental education is a relational view of learning. A purpose of this paper is to explore how such a view of learning is tied intimately to a person’s identity/subjectivity, that is, to explore how what happens ‘out there’ and ‘in here’ are mutually constitutive.
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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 | 0.020 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.017 | 0.104 |
| Scholarly communication | 0.021 | 0.031 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".