Psychogeography and Digital Semiosis: Walking Back(in)wards with Lapis Lazuli, the Transhistorical and Transregional Blue
Bibliographic record
Abstract
Through digital exploration, urban meandering, virtual strolls, and recent research in multimodal anthropology, this study cultivates a multidisciplinary and polysemiotic mapping approach while drawing on oral and written history, geography, etymology, and comparative epistemologies between two sites of semiosis: deep historical relations surrounding Sar-eSang, Afghanistan, and meaningful encounters in contemporary Toronto, Canada. In the process, my ongoing historical studies into the origins of the color blue evolve into a psychogeographic mapping of the myriad ways that the semi-precious stone lapis lazuli connects diverse cultures across the globe. Building on layers of digital excavation and archival research, art historical texts, and semiotic and semantic analysis of the concept of blueness in transhistorical and transregional perspective, my exploration becomes increasingly self-reflective, and my processes of understanding coalesce as an unfinished work of art through documenting sitescapes, walkscapes, mind maps, conversations, and creative reflections. This approach resists the exhibition mentality of showcasing a finished product by focusing instead on the journey itself—the semiotic relations, the in-between. This reversal also enables me to highlight critical aspects of digital materiality that would otherwise fade into the background, including the concepts of “anti-image” or “non-photograph,” and their relationships with LiDAR scanning, 3D mapping, and artificial intelligence. Paying close attention to interpersonal bodily experience and relational bonds in such hyper-mediated contexts becomes its own kind of resistance and transformational experience, encouraging further openings toward aesthetic cognition and non-Western epistemology.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.043 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".