Selection, sequencing and progression of content in biology in four diverse jurisdictions
Bibliographic record
Abstract
Selection of content for a school syllabus is important in achieving progress towards inclusive generalisations which characterise powerful knowledge. Biology as a discipline progresses from knowledge of individual facts to inclusive generalisations such as homeostasis, energy transformations, heredity, and evolution. The present study evaluated the selection of content in the official biology syllabus for the seventh and eighth years of schooling in four diverse jurisdictions: Kenya, South Africa, British Columbia (Canada) and Singapore. The purpose was to determine whether and how content selection enabled progression to inclusive generalisations in biology and to compare selection, sequencing and progression among the four jurisdictions. General Topic Trace Mapping was used to compare each syllabus to a generic reference syllabus structured according to inclusive generalisations. Although there was some agreement in the scope of topics selected, jurisdictions varied in the way it was organised. Kenya included more everyday knowledge than other jurisdictions. British Columbia and Singapore selected content according to unifying themes, whereas South Africa and Kenya did not. South Africa selected content that enabled progression towards inclusive generalisations, but did not explicitly identify the generalisations. This study supports the contention that powerful knowledge in biology may be construed differently in diverse jurisdictions.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".