What aspects of historical understanding feature in the analysis of moving-image sources in the history classroom?
Bibliographic record
Abstract
This paper reflects on aspects of historical understanding developed in a classroom in which moving-image sources are analysed. Considered as non-fictional representations of the past, moving-image sources comprised broadcast images of historical events on newsreels, news broadcasts and documentaries. The study, carried out in a Maltese state secondary school, involved students (aged 15/16 years) analysing moving images as historical sources in their history lessons. Various aspects of understanding were identified: making connections with media content; using knowledge of one topic to shape another; discussing forms of historical knowledge in relation to each other; connecting with the wider historical picture; and constructing meaning using various language strategies. It is argued that these aspects offer a characterization of historical understanding when analysing broadcast footage of historical events in a constructivist classroom. It is suggested that underlying these aspects was students’ prior historical knowledge. I highlight the importance of maximizing on opportunities provided by moving-image sources to support understanding, particularly the co-construction of knowledge.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".