‘Enculturation’: Acquisition of conceptual blind spots and epistemological prejudices
Bibliographic record
Abstract
Abstract Traditional science teaching has relied on ‘chalk and talk’. In recent years, ‘authentic science’ has become an alternative slogan that many educators easily adopted into their pedagogic discourses, for it was associated with ‘getting students to do the real stuff’. However, authentic science when it is not accompanied by reflection on representations of knowledge more generally, can also mean to enculturate (and worse, indoctrinate) students to a particular epistemology. In this article, the author provides two examples of invisible ways in which students of ecology are enculturated to particular ideologies. The unreflected matter‐of‐factness of the discursive and mathematical representations in lectures and textbooks makes the world appear to be typologically decomposable (into variables) which have clear, mathematically fully determined relationships (topologies). In this way, school science has a certain likeness with indoctrination. The author concludes by suggesting that science (or mathematics, history etc.) courses need to have built in moments in which students can critically examine disciplinary knowledge representations and the way these are constituted.
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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 | 0.021 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.024 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".