Prepared by: ART BAUERToward Bias Comprehensiveness and the Infusion of a STS Curriculum Emphasis Within
Bibliographic record
Abstract
Bias comprehensiveness adopts the inclusion of a multi-perspective approach that acts as a framework for exploring science, technology, and society issues. As Alberta moves to include the assigning of curriculum emphases to each major unit in the senior high science program how will the STS component be included within curriculum documents and evaluated through standardized diploma exams? As a discussion paper, attempts are made to generate questions that may serve to offer insights on the issues at hand. Current curriculum documents are examined and linked to research on science technology and science education. Beginning with an examination of current STS connections within the existing senior high science program, several questions may be proposed as to the degree to which the STS curriculum emphasis may be implemented and necessarily achieved. Alberta’s documents on curriculum emphases and education are useful in setting the course for the realization of a science program that incorporated the goals of STS education. What is proposed is a framework for bias comprehensiveness that brings together multiple perspectives on complex STS issues that may guide the student to a greater appreciation of the relationship between self, science and society. Purposes and Goals For STS Education
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 | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.031 | 0.005 |
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".