What does Rachel Carson have to do with family sociology and family policies? Ecological imaginaries, relational ontologies, and crossing social imaginaries
Bibliographic record
Abstract
In the past decade, multiple compounding crises – ecological, racial injustices, ‘care crises’ and multiple recent crises related to the COVID-19 pandemic – have reinforced the powerful role of critical and social policy researchers to push back against ‘fake news’, ‘alternative facts’, and a post-truth era that denigrates science and evidence-based research. These new realities can pose challenges for social scientists who work within relational, ontological, non-representational, new materialist, performative, decolonising, or ecological ‘turns’ in social theory and epistemologies. This article’s overarching question is: How does one work within non-representational research paradigms while also attempting to hold onto representational, authoritative and convincing versions of truth, evidence, facts and data? Informed by my research on feminist philosopher and epistemologist Lorraine Code’s 40-year trajectory of writing about knowledge making and ecological social imaginaries, I navigate these dilemmas by calling on an unexpected ally to family sociology and family policy: the late American environmentalist Rachel Carson. Extending Code’s case study of Carson, I argue for an approach that combines (1) ecological relational ontologies, (2) the ethics and politics of knowledge making, (3) crossing social imaginaries of knowledge making and (4) a reconfigured view of knowledge makers as working towards just and cohabitable worlds.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.035 | 0.039 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.004 | 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".