Science & Technology Education for Personal, Social & Environmental Wellbeing: Challenging Capitalists’ Consumerist Strategies
Bibliographic record
Abstract
There are many lenses through which we can examine science and science education. Drawing from a critical political perspective, this paper argues that school science and fields of professional science and technology are cooperatively-enmeshed in a global economic system prioritizing enrichment of few capitalists while compromising the wellbeing of many individuals, societies and environments. Under neoliberalism, for example, governments and extra-national organizations like the World Trade Organization promote strategic (non-)intervention in markets (regarding, for example, resource extraction, manufacturing, transportation and advertising) aimed at maximizing private profit, facilitated in part through externalization of personal, social and environmental costs. A major feature of this apparent system appears to be emphasis on creation of elastic and enthusiastic consumer desires — particularly among those with few needs — that may repeatedly occlude profitable compromises associated with commodities. Cycles of utopian identities mask dystopian realities. Images of community, sexuality and power, for instance, may distract ‘smart’ phone users from environmental hazards of toxins (e.g., lead, bromine, chlorine, mercury and cadmium) within; and, as well, social justice concerns for workers in associated mining and manufacturing. Such consumerism, with its emphasis on cycles of acceptance of chameleon-like Trojan Horses, seems to be partly facilitated by school science. Fields of science are, for example, portrayed as overly systematic, unbiased and unproblematic while, often, their professional practices may be compromised through capitalist partnerships and influences — alliances that often appear to contribute to many socio-scientific issues. At the same time, learners may become alienated from opportunities to self-determine agents of being important to them and their communities. Drawing on concepts associated with liberatory pedagogy, a case study of a radical science teacher whose promotion of student-led, research-informed, actions to address critical socio-scientific issues seem to counter tendencies towards consumerism and associated potential personal, social and environmental problems are discussed.
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.062 |
| Scholarly communication | 0.018 | 0.011 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.007 | 0.007 |
| 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".