One image, multiple nationalisms:<scp><i>F</i></scp><i>ace to</i> <scp><i>F</i></scp><i>ace</i>and the<scp>S</scp>iege at<scp>K</scp>anehsatà:ke
Bibliographic record
Abstract
Abstract Iconic news photographs, particularly those taken during wars and national crises, provide visual synopses of important historical events – events about which stories of triumph and tragedy are superimposed. In this paper, we systematically trace the appearances and discussions of a single, iconic image, given the moniker F ace to F ace , over time. In the twenty plus years since its initial publication, media discourses around the image referenced Kanien'kehaka / M ohawk, Indigenous, Q uebecois and C anadian nationalisms. We conclude that discourses surrounding war and conflict imagery can be read as reflecting plural nationalisms and that while a dominant meaning can be projected onto such imagery, this is neither singular nor fixed.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".