What’s important about...? Sources and evidence
Bibliographic record
Abstract
In this timely article, Ailsa Fidler and Chris Russell explore the use of sources and evidence in the teaching of primary history. Referring to Ofsted’s history subject report (July 2023), Ailsa and Chris explore how sources can be used effectively in the classroom and how children’s understanding of the role of the historian can be developed. Sources are traces of the human past. Without historical sources we have no history, nothing to build a picture of the past from, nothing to help us try to answer our questions about what came before. Of course, we can never have a complete picture because we do not have access to everything that was ever made, written or more importantly, thought. Sources come in many forms (the following are not exhaustive lists): Written – diaries, census returns, trade directories, newspapers, government records, legislation, letters/telegrams/postcards. Visual – maps, plans, paintings or photographs from the period, advertisements, posters, moving film. Other – oral accounts, music, radio programmes, speeches, buildings, statues, everyday articles such as household goods or clothing, historical sites
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 | 0.117 | 0.509 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.020 | 0.022 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.026 | 0.027 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".