“The many tinted woods”: building online teacher resources with photography collections
Bibliographic record
Abstract
This thesis aims to answer the question: how can photography collections be used as interpretative tools to build visual and media literacy skills through creative learning opportunities aligned with the Ontario education curriculum? The project has two components: an analytical paper and a teacher resource – created according to the Art Gallery of Ontario standard – to introduce teachers to teaching with photographs through interdisciplinary lessons in the visual culture of Canada from 1860 to the early 1900s. An analysis of the Ontario curriculum documents, identifying both limitations and benefits, and aims to support grade 7 and 8 teachers in the classroom are included. Using Canadian photographs from the AGO’s collection unites arts education and visual literacy with core academic subjects by prompting students, through a range of activities to engage with the subjects, aesthetic elements, history and materials of photographic media, and thus to interpret daily life at this time.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".