Imaginary Postcards: Flexi(Ble) Storytelling of a (Lost & Found) Canadian Myth- Making
Bibliographic record
Abstract
This essay approaches Flexi’s paintings as ‘imaginary postcards’ designing an ideal map of a certain Canadian collective identity, in fact a ‘cultural cliché’. Conceived for the market and for a series of customers (both locals and travellers), Flexi’s paintings hanged in a variety of places (restaurants, diners, people’s homes, hotels and more) and grew into a peculiar landscape themselves: they became part of an everyday scenario reminding people of what Canada should (ideally) be. Brought together for the first time in the form of an exhibition, these paintings reveal a more complex reality and even convey a reversed message: they show us what Canada in fact, never truly was, if not in imagination or myth-making. One of Marshall McLuhan’s laws of media postulates that each medium pushed to its extreme flips into its reverse. Pushing the Canadian picturesque to its extreme by repeating it almost ad infinitum, Flexi’s imaginary postcards of his paradise on earth flip into an uncanny geography that distracts us and, therefore, hides much from our vista. They create a simulacrum at once pretty and strange, and unfold a fascinating narrative enlightening on our way of reading, interpreting or even simulating a given reality.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.017 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".