Picturing Pedagogy: Images, Teaching, and Development
Bibliographic record
Abstract
Images are powerful. They shape how we see and understand the world and, in the process, challenge (or reinforce) our assumptions and perspectives. The images we use in the classroom are no exception, whether used passively as visual aids or as a “medium through which active learning is energized.”1 In this article we embrace the “pictorial turn” in university teaching and reflect on the use of images when teaching “development.”2 Development is an area that typically attracts students with an internationalist orientation and who seek to make a positive change in the world. Yet the concept of development is fraught in historical and political economic terms. Its complexity is reflected in academic debates about developmental imageries and imaginaries and, in particular, in representing global poverty. We argue that, by using images carefully and reflectively, we can help students think critically about the development project’s history and imperial dimensions whilst nurturing their desire to either struggle against global injustices or improve life and livelihood in particular places. We write from the standpoint of teachers in postgraduate education in both law and cognate disciplines. Our aim is to equip students with the kinds of contextual understandings and critical intellectual tools which help them to become engaged agents of change.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.030 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".