Bibliographic record
Abstract
The use of textbooks as critical learning and teaching resources is reinforced in South Africa by the Department of Education’s Revised Annual Teaching Plans for Social Sciences, Term One. In Week One of Grade 6, the 2021 plan states that each learner must receive a Social Studies textbook and be taught the importance of taking care of them (DoE, 2021, p. 1), reinforcing the status of the textbook as an authority on history content and learning. Consequently, research into history textbooks is important. This study produced data about some textbook images which could potentially challenge learner’s ability to construct historical narratives and to think historically. This challenge lies in the way in which these images are used as they form part of the repertoire of historical evidence. Through different time periods we have seen how images and photographs have recorded the past. While images are used to assist leaners understand the past, not all the images are historical evidence. Some are presented as real but are actually drawings of an event, artefact or person. Textbook authors do not distinguish clearly between what is real or not, with some scenes ‘staged’ to create a sense of reality for understanding. The general audience or readership may be under the impression that all the contents of a history textbook are authentic but there should be an awareness of these tendencies. Teachers then know how to move learners when the images are unclear or unsupported in their contexts. Proper captioning and provenance is strongly recommended so that images which are evidence can be classified as historical sources and not just generic representations of the past.
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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".