Scientific literacy for a knowledge society
Bibliographic record
Abstract
The role of science education in preparing students for the world of work was taken seriously by OECD (1996a, 1996b) when it launched studies on how the world of work was changing, especially in science-related occupations. At about the same time, the Royal Society for the Arts, Industry, and Commerce initiated a similar study in Britain (Bayliss, 1998). Together these studies found that the nature of work in developed countries has changed in three ways: in kind, in the requirements for performance, and in the lack of permanence of one’s engagement. These changes in the world of employment are driven by new forms of information technology, design requirements, and technology transfer. For example, oral and written literacies are no longer adequate; digital literacy is a new essential. The resulting new knowledge enables innovation of processes and products, locally and globally. This new knowledge is increasingly becoming a primary source of economic growth in knowledge-based economies. Taken together, these changes characterize a Knowledge Society (Gilbert, 2005). A knowledge-based economy and a Knowledge Society create a need to re-examine SL in developed countries. The chapter begins with an account of the features of a knowledge-based economy and a Knowledge Society, as these features affect the interpretation of SL. A policy position about SL-in-action is developed and analyzed in terms of considerations that would confront decision makers who shape school science curriculum policy. Special attention is paid to the choices that exist in types of school science content, and to the powerful influence of educational assessment practices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".